Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.