Non-Local Means Filter

In image processing, the Non-Local Means (NLM) filter is a widely-used denoising technique that effectively reduces noise while preserving image details. The algorithm operates by identifying similar pixel patches across the entire image and computing their weighted averages. This approach minimizes noise without blurring textures or edges, making it particularly effective for preserving fine structures. The NLM filter's implementation typically involves comparing patches using Euclidean distance metrics, with Gaussian-weighted similarity calculations to determine averaging weights. Key parameters include patch size and search window radius, which balance denoising strength and computational efficiency. This method finds extensive applications in image denoising, enhancement, and medical imaging, where detail preservation is critical. Code implementations often optimize patch comparison through integral images or dimensionality reduction techniques to handle computational complexity.