Skip to content
opencv-wasm

Gaussian blur

Algorithm atlasFiltering

Replace each pixel with a weighted average that gives nearby pixels more influence.

Try it on your image ↓

VISUAL WALKTHROUGHFiltering
REFERENCE INPUTNoisy or uneven image

The same scene stays here while the working view changes.

RESULTApply the rule at every pixel

The whole image has now been processed. The highlighted input value 156 becomes 136.8.

STEP 03 / 03

Sum the weighted values, then move the kernel to the next pixel.

Computed teaching example on a 24 × 16 image. Small kernels and simplified settings keep each change visible; use the image laboratory for native OpenCV.

Try it on an image

YOUR IMAGE · REAL OPENCV

Experiment at pixel level

Open full lab ↗

Smooth the colour image. Sigma controls the spread; kernel width limits its support.

The engine loads on your first run. Your images stay in this browser.

Input448 × 320
OutputWaiting for a result

Scroll over either image to zoom at the pointer. Use the scrollbars to pan both views over the same relative area. Zoom is relative to the input; pixel coordinates belong to each image. Warps can change scene correspondence.

Pixel inspector RGBA · native values · matched scale · 9 × 9 output pixels
Hover to inspect. Click to pin a pixel.
Input 
Select a pixel

Output 
Select a pixel

Sample models and licenses

When to use it

Reduce sensor noise before finding edges, build image pyramids, or soften detail at a controlled scale.

How it works

  1. 01Place a bell-shaped kernel around the current pixel.
  2. 02Multiply neighbours by their distance-dependent weights.
  3. 03Sum the weighted values, then move the kernel to the next pixel.

G(x,y) ∝ exp(−(x²+y²) / (2σ²))

What to tune

sigmaX controls the spatial scale; ksize limits the support. Use an odd kernel size, or let supported zero dimensions derive it from sigma.

Where it breaks down

Strong smoothing removes small features and blends across object boundaries. Border modes change the values near the image edge.

TypeScript API

Open an entry for its exact overloads, parameter descriptions, result ownership and pinned upstream source.

GaussianBlurReference →getGaussianKernelReference →

All of these calls execute on the CPU. Native objects need explicit disposal. See matrices and ownership and build compatibility.

Box filtering

Average a rectangular neighbourhood with equal weight at every position.

Median filtering

Choose the middle value of a pixel neighbourhood instead of averaging its intensities.

Bilateral filtering

Smooth nearby pixels only when their colours are also similar.

Custom correlation kernels

Apply your own linear filter to every pixel neighbourhood.