figo_飞哥 · Bilibili · 5:19
This video demonstrates how to visually approximate a Reaction-Diffusion Turing Pattern using standard Adobe Photoshop filters. The process involves generating an initial random noise field using the Clouds filter, introducing perturbations with the Add Noise filter, extracting spatial frequencies via a High Pass filter, and finally binarizing the result using a Threshold adjustment. While not a true numerical simulation of the partial differential equations, this sequence of image processing operations mimics the underlying mechanisms of pattern formation, yielding complex, maze-like structures characteristic of Turing patterns. This 120-second silent screen tutorial demonstrates a Photoshop workflow for creating a Turing-like black-and-white pattern from grayscale noise. It shows a Gaussian Blur step, a recorded Action that repeats High Pass, Threshold, and Gaussian Blur, the emergence of a maze-like pattern after repeated F2 executions, an Unsharp Mask cleanup pass, regeneration of a new pattern by editing the Clouds smart filter, and a final alternate experiment using Add Noise and Ripple distortion. This video demonstrates a creative method for simulating mathematical Turing patterns (reaction-diffusion systems) using iterative image processing filters in Adobe Photoshop. By repeatedly applying a sequence of Gaussian Blur, High Pass, and Threshold filters to an initial noise texture, complex, organic black-and-white labyrinthine structures emerge. The generated patterns are then showcased as fills for typography in graphic design posters.
Use the learning inspector for key ideas and moments, or open the reading tabs for the complete notes.
Generated from the video's visuals and explanation; not verbatim speech.
The video opens by displaying the title 'REACTION-DIFFUSION TURING PATTERN', followed by two static examples of complex, black-and-white maze-like structures. These images establish the visual goal of the tutorial: to recreate the characteristic patterns arising from reaction-diffusion systems.
The scene transitions to the Adobe Photoshop interface. The user creates a new 1000x1000 pixel document. To ensure non-destructive editing, the background layer is unlocked and converted into a Smart Object. The foreground and background colors are explicitly set to black and white, a crucial prerequisite for the upcoming steps.
The mathematical simulation begins with initialization. The user applies the 'Clouds' filter (Filter > Render > Clouds), which replaces the solid white canvas with a smooth, random grayscale noise texture. This represents the initial homogeneous state with random fluctuations. Next, the 'Add Noise' filter is applied (Filter > Noise > Add Noise) with an amount of 25%, uniform distribution, and monochromatic setting. This step injects high-frequency, pixel-level perturbations into the field, breaking perfect symmetry and providing the seeds for pattern formation.
To streamline the process, the user opens the Actions panel and records a new action named 'make turing pattern'. This captures the filter applications so they can be repeated or adjusted easily, though it is a software workflow optimization rather than a mathematical step.
The core spatial operation is performed next. The user applies the 'High Pass' filter (Filter > Other > High Pass) with a radius of 6.0 pixels. Visually, this flattens the image to a neutral gray while preserving the edges and rapid changes in the noise field. Mathematically, this approximates the computation of local spatial derivatives or Laplacians, which govern the diffusion component of the reaction-diffusion equations. Finally, the 'Threshold' adjustment (Image > Adjustments > Threshold) is applied at level 127. This forces every pixel to be either pure black or pure white based on whether its value is above or below the midpoint, discretizing the continuous result into the final binary Turing pattern.
The clip opens on a Photoshop canvas filled with fine grayscale noise. On the right, the Layers panel shows Layer 0 already carrying a stack of Smart Filters, including Threshold, High Pass, Add Noise, and Clouds. This establishes the starting condition: a random field that will be transformed iteratively.
The user opens Filter > Blur > Gaussian Blur. The dialog shows Radius: 3.0 Pixels, and the preview makes the noise visibly softer. In this workflow, the blur reduces high-frequency randomness so that later thresholding can produce larger coherent black-and-white regions.
On-screen text states, "We will repeat these three filter to create turing pattern." The Smart Filters list shows Gaussian Blur, Threshold, and High Pass together with the other filters. The exact Threshold and High Pass parameter values are not opened in this excerpt, but their presence in the stack confirms they are part of the repeated cycle.
The Actions panel appears with a set named Set 1 and an action named make turing pattern assigned to F2. Inside it are High Pass, Threshold, and Gaussian Blur. Text says, "So we use Action to repeat it," followed by "Stop Recording" and "The Action ready to use." This packages the filter sequence into a single repeatable operation.
The instruction changes to "Press F2 and Repeat until you get the Pattern." As the action is executed repeatedly, the canvas transitions from soft gray noise to a stark black-and-white labyrinth. By about 40 seconds, the caption reads "You'll get Turing Pattern like This," matching the visible maze-like result.
The Layers panel now shows a long repeated stack of Gaussian Blur, Threshold, and High Pass entries, visually confirming that the same transformation has been applied many times. The caption "So bunch of filter apply to the layer" summarizes this accumulation.
To improve legibility, the user opens Filter > Sharpen > Unsharp Mask. The dialog shows Amount: 200 %, Radius: 200.0 Pixels, Threshold: 0 levels. After confirmation, the maze edges become crisper and the black-white separation looks cleaner, while the overall pattern topology stays the same.
The next caption says, "You can change the pattern by double click on Cloud Filter." Double-clicking Clouds in the Smart Filters list regenerates the underlying random field. Because the rest of the filter stack remains unchanged, the output becomes a different Turing-like maze from a new random seed.
The tutorial then shifts to experimentation. A new layer is created above the existing one and converted to a Smart Object. The canvas briefly goes dark while the new layer is prepared, indicating a separate test path rather than a continuation of the first pattern.
Filter > Render > Clouds is applied, producing a soft grayscale cloud texture. Next, Filter > Noise > Add Noise opens with Amount: 25 %, Distribution: Uniform, and Monochromatic checked. This adds fine monochrome grain back into the smooth cloud field.
The caption says, "You can use any type of Distort you like." The user chooses Filter > Distort > Ripple. The Ripple dialog shows Amount: 999 % and Size: Large. After OK, the cloud-noise image is stretched into strong diagonal waves, demonstrating how a distortion filter can radically alter the texture.
The clip ends on the rippled grayscale result, with the Layers panel showing the new Smart Object stack containing Ripple, Add Noise, and Clouds above the original pattern layer.
The video begins by showing a grayscale canvas filled with random noise textures. On the right, the Layers panel reveals a stack of Smart Filters including Clouds, Ripple, and Add Noise. An Action named 'make turing pattern' is highlighted, containing steps for High Pass, Threshold, and Gaussian Blur.
Text on screen instructs to 'Press F2 again until you get the pattern'. As the action is triggered repeatedly, the image transforms. The Gaussian Blur smooths the noise, the High Pass extracts edges, and the Threshold forces pixels into strict black or white. This cycle repeats, causing local variations to amplify and organize into a complex, maze-like structure.
To refine the generated pattern, the user opens the Filter menu, navigates to Sharpen, and selects Unsharp Mask. The dialog box shows aggressive settings: Amount at 200%, Radius at 200.0 Pixels, and Threshold at 0 levels. Applying this sharply defines the boundaries of the black and white regions, removing any lingering gray artifacts.
The scene transitions to a presentation of the final results. Two square cards are displayed against a vibrant yellow background. The cards feature the generated black-and-white Turing patterns. Typography is overlaid on these patterns, with the words 'BE POSITIVE' and 'BE BRAVE' filled using the intricate maze textures, demonstrating the application of mathematical simulations in graphic design.
The video concludes with a minimalist white outro screen displaying music credits for 'Chill' by Sakura Hz, followed by an Instagram handle '@iwainobuyuki' and a final logo animation for 'NOBU DESIGN'.
A spatial pattern that arises from a mathematical model describing the interaction of chemical substances undergoing both local reactions and diffusion. Named after Alan Turing, these patterns typically manifest as stripes, spots, or mazes.
Generates a random grayscale noise pattern. In this workflow, it provides the initial random state required for the reaction-diffusion simulation to begin forming structures.
Adds random pixel variations (set to 25% Uniform Monochromatic here). This introduces the necessary microscopic fluctuations to break symmetry and allow the macroscopic Turing pattern to emerge.
Retains only high-frequency components (edges) of the image (Radius 6.0px). This step mathematically approximates the calculation of local spatial differences, simulating the diffusion process in the reaction-diffusion system.
Converts the grayscale result into a strict black-and-white image (Level 127). This final step discretizes the continuous mathematical output into the visually distinct pattern.
The core method is to start from grayscale noise and repeatedly apply a fixed filter cycle using an Action. The visible cycle includes Gaussian Blur, Threshold, and High Pass, and repeated execution with F2 turns soft noise into a black-and-white maze-like pattern.
The Gaussian Blur step is shown with Radius: 3.0 Pixels. Its role in this workflow is to smooth the initial random field before binarization and edge enhancement.
The repeated filter sequence is stored as an Action named make turing pattern and bound to F2. This allows the same transformation to be applied many times quickly, which is essential for the pattern to emerge.
After the maze pattern forms, Unsharp Mask is used to make the edges clearer. The visible settings are Amount: 200 %, Radius: 200.0 Pixels, Threshold: 0 levels.
Because the layer is a Smart Object, double-clicking the Clouds smart filter resamples the underlying random field. Keeping the rest of the filter stack fixed but changing Clouds produces a different final Turing-like pattern.
In the later test, Add Noise is applied to a cloud-generated grayscale image with Amount: 25 %, Distribution: Uniform, and Monochromatic enabled. This creates a fine monochrome grain before distortion.
The final distortion example uses Ripple with Amount: 999 % and Size: Large. This turns the noisy cloud field into a strongly wavy, directional texture.
This method approximates reaction-diffusion systems by iteratively applying linear smoothing (Gaussian Blur), edge detection (High Pass), and non-linear binarization (Threshold) to random noise. The repetition allows local interactions to propagate, forming global, self-organizing spatial patterns.
After generating the base pattern, an Unsharp Mask filter with high Amount (200%) and large Radius (200px) is used to maximize contrast and sharpen the edges of the black and white regions, ensuring clean lines for design applications.
The resulting high-contrast, organic Turing patterns serve as unique texture fills for typography. By masking text layers with the generated images, designers can create visually striking posters that blend mathematics with art.
Explore conditions, steps and evidence. Supplementary explanations are labeled separately from content shown in the video.
Gaussian Blur dialog shows Radius: 3.0 Pixels.
Radius
Blur radius parameter in the Gaussian Blur filter.
Pixels
Unsharp Mask dialog shows Amount: 200 %.
Amount
Sharpening amount parameter in the Unsharp Mask filter.
%
Unsharp Mask dialog shows Radius: 200.0 Pixels.
Radius
Sharpening radius parameter in the Unsharp Mask filter.
Pixels
Unsharp Mask dialog shows Threshold: 0 levels.
Threshold
Edge threshold parameter in the Unsharp Mask filter.
levels
Add Noise dialog shows Amount: 25 %.
Amount
Noise intensity parameter in the Add Noise filter.
%
Add Noise dialog shows Distribution: Uniform and Monochromatic checked.
Distribution
Probability distribution used by the Add Noise filter.
Uniform / Gaussian
Ripple dialog shows Amount: 999 %.
Amount
Distortion strength parameter in the Ripple filter.
%
Ripple dialog shows Size: Large.
Size
Wavelength category parameter in the Ripple filter.
Small / Medium / Large
The text 'TURING PATTERN - 1' and 'TURING PATTERN - 2' appears in the bottom right corner of the final rendered images.
Turing Pattern
A spatial pattern that arises from a reaction-diffusion system, here simulated using image processing filters.
Applied Mathematics / Computer Graphics
The Layers panel shows 'Gaussian Blur' as a Smart Filter applied repeatedly to Layer 1.
Gaussian Blur
A low-pass filter that smooths an image by convolving it with a Gaussian function, reducing high-frequency details.
Image Processing
The Layers panel shows 'High Pass' as a Smart Filter applied repeatedly to Layer 1.
High Pass
A filter that extracts the edges and fine details of an image by subtracting a blurred version of the image from the original.
Image Processing
The Layers panel shows 'Threshold' as a Smart Filter applied repeatedly to Layer 1.
Threshold
A non-linear operation that converts a grayscale image into a binary image by setting pixels above a certain value to white and below to black.
Image Processing
The text 'REACTION-DIFFUSION TURING PATTERN' is displayed on the screen.
A reaction-diffusion system is a mathematical model that describes the change in concentration of one or more substances under the influence of two processes: local chemical reactions and diffusion. A Turing pattern is a spatial pattern that arises from such a system, named after Alan Turing who proposed the mechanism.
Requires at least two interacting substances with different diffusion rates.
The user navigates to Filter > Render > Clouds, and a grayscale noise pattern appears.
The Clouds filter generates a random grayscale noise pattern based on the foreground and background colors. It serves as the initial state for the reaction-diffusion simulation.
Foreground and background colors must be set to black and white to generate grayscale noise.
The user navigates to Filter > Noise > Add Noise, sets Amount to 25%, Distribution to Uniform, and checks Monochromatic.
The Add Noise filter adds random pixel variations to an image. In this context, it introduces small perturbations to the initial smooth noise field, which are necessary to break symmetry and allow the Turing pattern to emerge.
Amount is set to 25%.
Distribution is set to Uniform.
Monochromatic is checked.
The user navigates to Filter > Other > High Pass, sets Radius to 6.0 Pixels, and the image turns mostly gray with faint edges.
The High Pass filter retains only the high-frequency components of an image, effectively removing smooth gradients and leaving edges and fine details. Here, it acts as a spatial operator to compute local differences in the noise field.
Radius is set to 6.0 Pixels.
The user navigates to Image > Adjustments > Threshold, sets Level to 127, and the image becomes strictly black and white.
The Threshold adjustment converts a grayscale image into a binary (black and white) image by setting all pixels below a certain level to black and all pixels above to white. This step discretizes the continuous values resulting from the high-pass filter into the final pattern.
Level is set to 127.
On-screen text says: We will repeat these three filter to create turing pattern.
On-screen text says: Press F2 and Repeat until you get the Pattern.
The Layers panel lists Gaussian Blur, Threshold, High Pass, Add Noise, Clouds under Smart Filters.
The video demonstrates a Photoshop workflow for generating a Turing-like pattern by repeatedly applying a fixed sequence of filters to a noisy grayscale image. The visible filter stack includes Gaussian Blur, Threshold, High Pass, Add Noise, and Clouds, and the repeated application is performed through an Action bound to F2.
Start from a grayscale noise image.
Convert the layer to a Smart Object so filters remain editable.
Repeat the recorded filter sequence until a maze-like pattern emerges.
Gaussian Blur dialog shows Radius: 3.0 Pixels.
The noisy image becomes smoother after the Gaussian Blur preview is applied.
A Gaussian Blur with radius 3.0 pixels is applied to the initial noise field. In this workflow it smooths local intensity variations before binarization and edge enhancement.
Applied to the current grayscale image layer.
Threshold appears in the Smart Filters list.
After repeated filter cycles, the image changes from gray noise to black-and-white regions.
The exact Threshold parameter value is not shown in this clip.
Threshold converts the smoothed grayscale image into a binary black-and-white image. This step is part of the repeated filter cycle that produces the Turing-like pattern.
Requires a grayscale input image.
High Pass appears in the Smart Filters list.
The video states that these three filters are repeated to create the turing pattern.
The exact High Pass parameter value is not shown in this clip.
High Pass is included in the repeated filter sequence. In this visual workflow it emphasizes local structure between blur and thresholding stages.
Used as one of the repeated filters in the pattern-generation cycle.
On-screen text says: So we use Action to repeat it.
Actions panel shows Set 1 > make turing pattern F2 containing High Pass, Threshold, Gaussian Blur.
On-screen text says: Stop Recording and The Action ready to use.
The repeated filter sequence is recorded as an Action named make turing pattern and assigned to F2. Pressing F2 repeatedly applies the same transformation cycle many times.
The desired filter sequence must be recorded first.
The Action must be selected before playback.
On-screen text says: add Sharpen Filter to make pattern clearer.
Unsharp Mask dialog shows Amount: 200 %, Radius: 200.0 Pixels, Threshold: 0 levels.
After the pattern has formed, the video applies Unsharp Mask with Amount 200%, Radius 200.0 pixels, and Threshold 0 levels to make the black-and-white maze edges clearer.
Applied after the repeated Turing-pattern cycle.
On-screen text says: You can change the pattern by double click on Cloud Filter.
Double-clicking Clouds in the Smart Filters list regenerates a different noise base and therefore a different final pattern.
Because the layer is a Smart Object, double-clicking the Clouds smart filter regenerates the underlying random field. This changes the starting condition and yields a new Turing-like pattern after the same filter cycle.
The layer must remain a Smart Object with editable Smart Filters.
The image begins as fine grayscale noise, is blurred, then after repeated filter cycles becomes a high-contrast black-and-white maze-like pattern.
We will repeat these three filter to create turing pattern.
Press F2 and Repeat until you get the Pattern.
The clip does not show the mathematical reaction-diffusion equations behind Turing patterns; it only demonstrates a visual approximation in Photoshop.
Start from a grayscale noise image on Layer 0.
Visible initial canvas state at the beginning of the clip.
Apply Gaussian Blur with Radius 3.0 Pixels.
Shown in the Gaussian Blur dialog.
Use Threshold and High Pass as part of the repeated filter set.
Both appear in the Smart Filters list and in the recorded Action.
Record the sequence as an Action and repeat it with F2.
Actions panel and on-screen instructions show make turing pattern F2 and repeated execution.
After enough repetitions, the image changes from soft gray noise to a crisp black-and-white maze-like pattern.
Direct visual transformation shown around 34-41 seconds.
Repeated application of the recorded filter cycle transforms an initial noise field into a Turing-like black-and-white pattern.
add Sharpen Filter to make pattern clearer.
Unsharp Mask parameters: Amount 200 %, Radius 200.0 Pixels, Threshold 0 levels.
You can change the pattern by double click on Cloud Filter.
After the maze pattern is formed, apply Unsharp Mask.
The video explicitly adds a sharpen filter to make the pattern clearer.
Use Amount 200%, Radius 200.0 pixels, Threshold 0 levels.
These values are visible in the Unsharp Mask dialog.
Double-click Clouds in the Smart Filters stack to regenerate the base noise.
On-screen instruction and visible cursor action.
The final pattern changes because the underlying random field changed while the filter stack stayed the same.
Visual comparison before and after the Clouds regeneration.
Sharpening clarifies the existing pattern, and regenerating Clouds produces a different pattern from the same filter pipeline.
The Layers panel history shows the repeated application of Gaussian Blur, High Pass, and Threshold filters, progressively changing the image from a noisy gray texture to a sharp black-and-white maze-like pattern.
Start with a base layer filled with random noise (Clouds, Ripple, Add Noise).
Provides the initial heterogeneous state required for pattern formation.
Apply Gaussian Blur to smooth the noise, creating broad areas of light and dark.
Simulates the diffusion component, spreading values locally.
Apply High Pass filter to extract the edges and gradients from the smoothed image.
Highlights areas of rapid change, simulating the reaction component's sensitivity to local concentration differences.
Apply Threshold to convert the image into strict black and white pixels.
Introduces non-linearity, forcing intermediate values to one of two stable states, akin to a bistable chemical system.
Repeat the Blur -> High Pass -> Threshold sequence multiple times (using an Action macro).
Iteration allows the local interactions to propagate across the entire canvas, forming global structures.
The repeated application of linear smoothing, edge detection, and non-linear thresholding creates complex, self-organizing spatial patterns similar to Turing structures.
The entire sequence of Photoshop actions from creating a document to applying the threshold.
Generate a visual approximation of a Reaction-Diffusion Turing Pattern using standard image processing filters.
A blank 1000x1000 pixel canvas.
Black and white foreground/background colors.
Produce a black and white maze-like pattern.
Create a new 1000x1000 pixel document and convert the background layer to a Smart Object.
Smart Objects allow non-destructive application of filters, enabling parameter adjustments later.
Apply the Clouds filter (Filter > Render > Clouds).
Generates the initial random grayscale noise field.
Apply the Add Noise filter (Filter > Noise > Add Noise) with Amount 25%, Uniform distribution, and Monochromatic checked.
Introduces high-frequency perturbations to the noise field.
Apply the High Pass filter (Filter > Other > High Pass) with a Radius of 6.0 Pixels.
Computes local spatial differences, simulating the diffusion aspect of the reaction-diffusion system.
Apply the Threshold adjustment (Image > Adjustments > Threshold) with a Level of 127.
Binarizes the result to create the final distinct black and white pattern.
A black and white maze-like pattern resembling a Turing pattern.
The final visual output matches the examples shown at the beginning of the video.
Full demonstration from initial noise to finished black-and-white pattern.
You'll get Turing Pattern like This.
Create a Turing-like black-and-white pattern in Photoshop from a noisy grayscale image.
A grayscale noise image on Layer 0.
Smart Filters available: Gaussian Blur, Threshold, High Pass, Add Noise, Clouds.
An Action named make turing pattern assigned to F2.
Obtain a maze-like Turing pattern and then make it clearer or regenerate it.
Apply Gaussian Blur with Radius 3.0 Pixels.
Shown in the filter dialog.
Use the recorded Action containing High Pass, Threshold, and Gaussian Blur.
Visible in the Actions panel.
Press F2 repeatedly until the pattern appears.
Explicit on-screen instruction.
Apply Unsharp Mask with Amount 200%, Radius 200.0 Pixels, Threshold 0 levels.
Shown in the Unsharp Mask dialog.
Double-click Clouds to generate a different pattern if desired.
Explicit on-screen instruction and visible result change.
A high-contrast black-and-white Turing-like maze pattern is produced, and it can be clarified or regenerated.
The final canvas visibly changes from gray noise to a crisp maze pattern, and changing Clouds yields a different maze pattern.
You can experiment with another filter.
You can use any type of Distort you like.
A new layer is converted to a Smart Object, Clouds and Add Noise are applied, then Ripple distorts the image.
Experiment with another filter chain after making the Turing pattern.
A new layer above the existing pattern.
Clouds filter available.
Add Noise and Ripple filters available.
Create a different textured result using distortion filters.
Create Layer 1 and convert it to a Smart Object.
Visible in the Layers panel and context menu.
Apply Clouds to generate a soft grayscale field.
Shown in the Render menu and resulting canvas.
Apply Add Noise with Amount 25%, Distribution Uniform, Monochromatic checked.
Visible in the Add Noise dialog.
Apply Ripple with Amount 999% and Size Large.
Visible in the Ripple dialog.
The cloud-noise field becomes a wavy, streaked distorted texture.
The canvas visibly changes from soft clouds with noise to a strongly rippled pattern after OK is pressed.
Two final images are shown with yellow backgrounds and white cards featuring the generated black-and-white patterns. Text 'BE POSITIVE' and 'BE BRAVE' is overlaid using the pattern as a fill.
How can the mathematically generated Turing pattern be used in practical design?
A generated black-and-white Turing pattern image.
Design software (e.g., Adobe Illustrator/Photoshop).
Create visually appealing posters with text filled by the pattern.
Place the generated pattern image as a clipping mask or texture fill behind or inside typography.
Utilizes the organic, high-contrast nature of the pattern for aesthetic effect.
Posters reading 'BE POSITIVE' and 'BE BRAVE' where the letters are filled with the intricate maze-like Turing patterns.
The visual output at 00:52 confirms the successful application of the generated asset into a finished graphic design piece.
Two static images of complex black and white maze-like patterns are shown, labeled 'TURING PATTERN - 1' and 'TURING PATTERN - 2'.
Black and white maze-like patterns
The patterns remain static during their display.
These images serve as the target visual outcome for the tutorial, illustrating what a Turing pattern looks like.
The solid white canvas instantly changes to a smooth, cloudy grayscale texture.
White canvas
Grayscale noise texture
The uniform white color is replaced by varying shades of gray.
The dimensions of the canvas remain unchanged.
This visual change represents the initialization of the reaction-diffusion system with a random state.
The smooth grayscale texture becomes grainy with sharp, random pixel variations.
Grayscale noise texture
Grainy texture
Fine, high-frequency details are added to the existing smooth noise.
The overall macroscopic distribution of light and dark areas remains similar.
This step introduces the necessary microscopic fluctuations for pattern formation.
The grainy texture turns into a mostly flat gray image with faint, thin lines outlining the previous variations.
Grainy texture
Flat gray image with faint edges
Smooth gradients are removed, leaving only areas of rapid change (edges).
The spatial locations of the original features are preserved as edges.
This operation isolates the spatial frequency components relevant to the diffusion process.
The flat gray image with faint edges abruptly changes into a stark black and white pattern.
Gray image with edges
Black and white pattern
All mid-tone grays are forced to either pure black or pure white.
The underlying structure of the edges dictates the final black and white shapes.
This final step discretizes the continuous mathematical result into a binary visual pattern.
The canvas shows dense grayscale static noise.
Layers panel shows Layer 0 with Smart Filters: Threshold, High Pass, Add Noise, Clouds.
Grayscale noise image
Layer 0
Smart Filters list
No image transformation yet; the starting state is established.
The document remains a single-layer grayscale composition.
This is the raw random input from which the iterative pattern will be generated.
The Gaussian Blur preview window shows the noise becoming smoother.
Radius: 3.0 Pixels.
Gaussian Blur dialog
Preview thumbnail
Radius slider
Local high-frequency noise is reduced.
The image becomes softer and more continuous.
The overall grayscale character remains.
Blurring suppresses fine random detail so later thresholding can create larger coherent structures.
Actions panel shows Set 1 > make turing pattern F2 with High Pass, Threshold, Gaussian Blur.
So we use Action to repeat it.
Actions panel
make turing pattern
F2 shortcut
The manual filter sequence is packaged into a reusable Action.
The same three-filter cycle is preserved for repeated execution.
The Action makes iterative application practical and repeatable.
After repeated F2 executions, the canvas changes from gray noise to a black-and-white maze-like pattern.
You'll get Turing Pattern like This.
Canvas image
Smart Filters stack
Contrast increases sharply.
Continuous gray regions become discrete black and white domains.
The pattern becomes labyrinthine.
The same filter pipeline is being reapplied.
Repeated local smoothing, edge emphasis, and binarization amplify spatial structure from the initial noise.
The Unsharp Mask preview shows stronger edge contrast in the maze pattern.
Amount: 200 %, Radius: 200.0 Pixels, Threshold: 0 levels.
Unsharp Mask dialog
Maze pattern preview
Edges become crisper.
Black and white regions separate more clearly.
The overall topology of the maze remains the same.
Sharpening improves visual legibility without changing the underlying pattern generation process.
Double-clicking Clouds changes the base noise and the final pattern layout.
You can change the pattern by double click on Cloud Filter.
Clouds smart filter
Final pattern
The underlying random field is resampled.
The final maze pattern changes shape while using the same filter stack.
The Smart Object and its filter order remain intact.
The output depends on the initial random seed; changing Clouds changes the seed and therefore the pattern.
After Ripple is confirmed, the cloud-noise image becomes strongly wavy and streaked.
Amount: 999 %, Size: Large.
Ripple dialog
Distorted grayscale texture
Smooth cloud blobs are stretched into diagonal wave-like bands.
The texture becomes more directional and turbulent.
The image remains grayscale.
A distortion filter can radically alter the visual character of the same base noise field.
The video calls the result a turing pattern.
The process shown is a sequence of image filters rather than a explicit reaction-diffusion simulation.
Assuming the Photoshop filter loop is the same thing as solving Turing's reaction-diffusion equations.
The clip demonstrates a visual approximation that produces Turing-like patterns, but it does not present the underlying PDE model or its parameters.
Threshold and High Pass are listed in the Smart Filters stack, but their dialogs are not opened in this clip.
Assuming all filter settings needed to reproduce the result are shown on screen.
Only Gaussian Blur, Unsharp Mask, Add Noise, and Ripple parameter values are visible; Threshold and High Pass values are not shown in this excerpt.
The Add Noise filter is applied directly after the Clouds filter on the same layer.
The Add Noise filter is applied to the output of the Clouds filter to introduce necessary perturbations.
The High Pass filter is applied after the Add Noise filter.
The High Pass filter processes the noisy image to extract spatial frequency information.
The Threshold adjustment is applied after the High Pass filter.
The Threshold adjustment binarizes the output of the High Pass filter to create the final pattern.
Blurring precedes the binarized pattern formation in the repeated cycle.
Gaussian Blur, Threshold, and High Pass appear together in the Smart Filters and Action.
The blurred grayscale field is the input stage before thresholding creates the black-and-white structure.
So we use Action to repeat it.
Press F2 and Repeat until you get the Pattern.
The Action is the mechanism used to execute the repeated filter method that generates the pattern.
You can change the pattern by double click on Cloud Filter.
Editing Clouds changes the final maze layout.
Regenerating the Clouds layer changes the initial random field on which the same Turing-pattern method operates.
add Sharpen Filter to make pattern clearer.
Unsharp Mask is applied after the pattern exists to enhance its visual clarity.
You can experiment with another filter.
You can use any type of Distort you like.
The Ripple experiment contrasts with the main Turing-pattern cycle by using a different filter chain for a different visual effect.
The final output is explicitly labeled 'TURING PATTERN', linking the image processing technique to the mathematical concept.
The exact mathematical equivalence between the Photoshop filters and the Gray-Scott or other specific reaction-diffusion models is not proven in the video.
The iterative image filtering method is used as a computational approximation to generate visual examples of Turing patterns.
Title 'REACTION-DIFFUSION TURING PATTERN' and subsequent Photoshop tutorial.
Application of the High Pass filter with Radius 6.0.
We will repeat these three filter to create turing pattern.
Radius: 3.0 Pixels.
Amount: 200 %, Radius: 200.0 Pixels, Threshold: 0 levels.
You can change the pattern by double click on Cloud Filter.
Amount: 999 %, Size: Large.
The entire first minute of the video is a tutorial on this process.
The Unsharp Mask dialog box is clearly visible with specific parameter values.
Covered · Introductory title cards and visual examples of the target Turing patterns.
Covered · Setting up the Photoshop document and converting to a Smart Object. No specific mathematical concept introduced yet, but prepares the workspace.
Covered · Applying the Clouds and Add Noise filters to generate the initial perturbed state.
Covered · Recording an Action in Photoshop for automation. This is a software workflow step, not a mathematical concept.
Covered · Applying the High Pass filter and Threshold adjustment to compute the final pattern.
Covered · Initial noise image and existing Smart Filters stack are shown.
Covered · Gaussian Blur dialog and radius value are visible.
Covered · Smart Filters list shows Threshold and High Pass as part of the cycle.
Covered · Actions panel and recording stop are shown.
Covered · Repeated F2 execution produces the maze-like pattern.
Covered · Finished pattern and long Smart Filters stack are displayed.
Covered · Unsharp Mask is applied with visible parameters.
Covered · Double-clicking Clouds regenerates a different pattern.
Covered · The completed pattern remains on screen before the alternate experiment.
Covered · A new Smart Object layer is created and prepared for another filter experiment.
Covered · Add Noise dialog shows 25% uniform monochromatic noise.
Covered · Ripple is selected and applied with Amount 999% and Size Large.
Covered · Demonstration of the iterative filtering process to generate the pattern.
Covered · Showcase of the final Turing patterns applied to graphic design.
Covered · Outro sequence with music credits, social media handles, and logo animation. No mathematical content.
Reviewed subject paths
In the Turing pattern workflow, the Gaussian Blur filter is applied with a Radius of 3.0 Pixels. This step smooths the initial random field before binarization and edge enhancement, reducing high-frequency randomness so that later thresholding can produce larger coherent black-and-white regions.
Conditions: Applied to the current grayscale image layer.; Part of the repeated filter cycle for pattern generation.
To create a Turing pattern in Photoshop, you start with a grayscale noise image and repeatedly apply a fixed sequence of filters: Gaussian Blur, Threshold, and High Pass. This iterative process is automated using a recorded Action bound to the F2 key.
Conditions: Start from a grayscale noise image.; Convert the layer to a Smart Object so filters remain editable.; Record the filter sequence as an Action.; Repeat the recorded filter sequence until a maze-like pattern emerges.
Because the layer is a Smart Object, you can double-click the Clouds smart filter in the Layers panel. This regenerates the underlying random noise field (changing the random seed).
Conditions: The layer must remain a Smart Object with editable Smart Filters.; The Clouds filter must be part of the Smart Filters stack.
The Add Noise filter introduces small perturbations to the initial smooth noise field generated by the Clouds filter. These high-frequency, pixel-level variations are necessary to break perfect symmetry and provide the seeds for the macroscopic Turing pattern to emerge during the iterative filter cycle.
Conditions: Applied after the Clouds filter.; Amount is set to 25%.; Distribution is set to Uniform.; Monochromatic is checked.
In the alternate distortion experiment, the Ripple filter is applied with an Amount of 999% and a Size of Large. This turns the noisy cloud field into a strongly wavy, directional texture, demonstrating how a distortion filter can radically alter the visual character of the base noise field.
Conditions: Used as an optional distortion after the base pattern or noise field is created.; Applied to a grayscale Smart Object layer.
In this context, the High Pass filter retains only the high-frequency components of the image, effectively removing smooth gradients and leaving edges and fine details. Mathematically, it approximates the computation of local spatial derivatives or Laplacians, which govern the diffusion component of the reaction-diffusion equations.
Conditions: Radius is set to 6.0 Pixels.; Applied after the Add Noise filter.
After the Turing-like maze pattern has formed, the Unsharp Mask filter is applied with an Amount of 200%, a Radius of 200.0 Pixels, and a Threshold of 0 levels. This heavily enhances the contrast between the black and white regions, making the pattern lines sharper and more distinct without changing the underlying topology.
Conditions: Applied after the repeated Turing-pattern cycle.; Used to make the black-and-white maze edges clearer.
The Threshold adjustment converts the grayscale image into a binary (black and white) image by setting all pixels below a certain level to black and all pixels above to white. This step discretizes the continuous values resulting from the high-pass filter into the final distinct black and white pattern, acting as a non-linear operation akin to a bistable chemical system.
Conditions: Level is set to 127.; Applied after the High Pass filter.
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