Image denoising based on non-local means filter and its method noise thresholding

Non local-means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non local-means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non local-means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because, the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non local-means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non local-means filter, multi-resolution bilateral filter. It is found that, performance of proposed method is superior to wavelet thresholding, bilateral filter and non local-means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.