Fast Nonlocal Means Image Denoising Algorithm Using Selective Calculation

A fast nonlocal means (NLM) image denoising method with selective calculation is proposed to solve the problem that the computational cost of similarity weights is high. By using L2 Norm successive elimination, a large number of pixels of low similarity van be rejected through a small amount of additive operations on integral image, and the massive calculation on measuring similarity can be effectively reduced. According to spatial coherence in the image domain, an approach for adaptive search area based on patch geodesic distance is proposed. Experimental results demonstrate that the proposed method, compared with the state-of-the-art algorithms, can not only accelerate the nonlocal means algorithm, but also elevate the image quality.