FILTER.js: image and video processing and computer vision in pure JavaScript

What FILTER.js offers

FILTER.js is a library for image and video processing, filtering and computer vision written in pure JavaScript, comparable in aim to OpenCV but native to the JavaScript world. It runs both in the browser, using HTML5 features such as Canvas, Web Workers, WebAssembly and WebGL, and in Node.js, using equivalents like CanvasLite, node-canvas, node-gl or worker processes. The capability set the topics list is broad for a JavaScript library: color and convolution filters, Canny edge detection, connected components, feature and shape detection, template matching and real-time processing. The audience is web and Node developers who want image processing or computer vision inside a JavaScript application without pulling in a large native or WebAssembly port of OpenCV. Its distinguishing idea is being pure JavaScript with pluggable acceleration, so it fits naturally into browser and Node projects.

Pure JavaScript with pluggable acceleration

The mechanism is a JavaScript core that can accelerate through the platform features available to it. In the browser it can offload work to Web Workers for parallelism, use WebGL and GLSL shaders for GPU-accelerated filters, and use WebAssembly where that is faster, falling back to plain Canvas operations otherwise. In Node.js it uses server-side equivalents such as node-canvas and node-gl, or spawns processes for parallel work. This layered approach lets the same library run wherever JavaScript does while taking advantage of acceleration when present, which is the point of being pure JavaScript rather than a binding to a native library: no compilation step and no platform-specific binary, at the cost of depending on what the runtime offers. The filters and computer vision operations are implemented over image data the library reads from Canvas or equivalent sources.

Adding FILTER.js to a project

FILTER.js is a JavaScript library you include rather than install through a build step in the usual package sense, and the README frames it around browser and Node.js usage. In the browser you load the built library from the repository's build directory and apply filters to image data obtained from a Canvas, and the project's online examples demonstrate the operations interactively. In Node.js you require the library and pair it with a Canvas implementation such as CanvasLite or node-canvas to supply and receive image data. The repository ships the built files, an API reference and an examples folder, which are the starting points for wiring it in. The first real use is loading an image into a Canvas, applying one of the library's filters, such as an edge detector, and rendering the result, which confirms the setup and shows the filter pipeline before you combine operations.

Where a pure-JavaScript CV library has limits, including no license

The first limitation to flag is licensing: the repository ships no license file, so default copyright applies and you have no explicit grant to reuse or redistribute the code, which is a real barrier for adopting it in a project and is worth resolving with the author. On the technical side, pure JavaScript, even with WebGL and WebAssembly acceleration, generally will not match a heavily optimized native OpenCV build for the most demanding workloads, so for large-scale or latency-critical computer vision the ceiling is lower. Its capabilities, while broad, are not the full breadth of OpenCV's decades of algorithms, so a specific advanced operation may not be present. And performance depends on the runtime features available, WebGL and WebAssembly support in the browser, or the Node canvas and GL packages you install, so behavior varies across environments. These are the trade-offs of staying in pure JavaScript.

FILTER.js versus OpenCV.js

The natural comparison is OpenCV.js, the official WebAssembly build of OpenCV for the browser. OpenCV.js brings OpenCV's extensive, battle-tested algorithm set to JavaScript, which is its strength, but it is a large WebAssembly module to load and it exposes OpenCV's C++ style API rather than an idiomatic JavaScript one. FILTER.js's difference is that it is written natively in JavaScript with a JavaScript-friendly design and pluggable Canvas, Worker, WebGL and WebAssembly backends, so it can be lighter to integrate and more natural to use, at the cost of narrower algorithm coverage and lower peak performance than a full OpenCV build. Choose OpenCV.js when you need OpenCV's specific algorithms or maximum capability and can accept the module size; choose FILTER.js when you want a native-JavaScript library for common image, video and computer vision operations that fits cleanly into a browser or Node project.

Licensing to resolve, and status

The status to weigh is that FILTER.js is a long-standing pure-JavaScript library with a broad feature set, online examples, an API reference and both browser and Node support, but it ships no license file, which is the single most important thing to settle before using it, since without a license you lack the right to reuse or redistribute it. The last push was on 2026-08-31, and it has a history of tagged releases, so it is maintained. Adopt it when you want image and video processing or computer vision in pure JavaScript across the browser and Node without a native dependency, load it from the build directory or require it with a Canvas backend, try an edge detector or filter on a Canvas image to confirm your environment, and clarify the licensing with the author before you build it into anything you ship.

Editorial conclusion

Adopt FILTER.js if you want image and video processing or computer vision in pure JavaScript across the browser and Node.js, with Canvas, Web Workers, WebGL and WebAssembly acceleration and no native dependency. Do not build it into anything you ship without first resolving the missing license, since default copyright applies, and do not expect it to match a full native OpenCV build for the most demanding workloads. Load it from the build directory or require it with a Canvas backend, apply a filter to a Canvas image to confirm your setup, and clarify licensing with the author.

Frequently asked questions

Official sources

1. foo123/FILTER.js on GitHub
2. Issues
3. Project website
4. README
5. Releases