fastNlMeansDenoising

Computational photographyfunctionOpenCV 5.0.0

import { fastNlMeansDenoising } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Non-local means denoising →

Average pixels whose surrounding patches look similar, even when they are not immediate neighbours.

VISUAL WALKTHROUGHPhotography

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 156.2.

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

photo_inpaint

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise

fastNlMeansDenoising(src: Mat, dst: Mat, h: number, templateWindowSize: number, searchWindowSize: number): void;

4 available overloads

fastNlMeansDenoising(src: Mat, dst: Mat): void;

fastNlMeansDenoising(src: Mat, dst: Mat, h: number): void;

fastNlMeansDenoising(src: Mat, dst: Mat, h: number, templateWindowSize: number): void;

fastNlMeansDenoising(src: Mat, dst: Mat, h: number, templateWindowSize: number, searchWindowSize: number): void;

src

Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.

dst

Output destination, filled by the native operation. Output image with the same size and type as src .

h

Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise

This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

templateWindowSize

Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels

searchWindowSize

Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels

Upstream declaration ↗

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