Noise reduction for images with non-uniform noise using adaptive block matching 3D filtering

Abstract

Noise reduction is a very important topic in image processing. We propose a new method to deal with the case where the noisy image has different noise levels in different regions. The main idea is to segment automatically the noisy image into several sub-images so that each sub-image has approximately the same noise level.We perform Block matching 3D filtering (BM3D) to these subimages in order to obtain denoised sub-images. We then merge sub-images together and enhance the discontinuous regions between the sub-images by performing BM3D again on small image patches. Our experimental results show the effectiveness of this proposed method in terms of Peak signal to noise ratio (PSNR) when compared with the bivariate wavelet shrinkage and the standard BM3D method. In addition to Gaussian white noise, our method performs better than the bivariate wavelet shrinkage and the standard BM3D method even for signal dependent noise.