Image Denoising Algorithm based on Improved NCSR Model

Abstract

The Nonlocally Centralized Sparse Representation (NCSR) algorithm aims to reduce sparse coding noise and utilizes the Nonlocal Self-similarity of the image to improve the effectiveness and accuracy of the image denoising algorithm, which has achieved good denoising effect. However, in the local dictionary learning and sparse coefficient estimation, the algorithm directly uses the Euclidean distance of the noise image to measure the similarity, which will have a certain impact on the final denoising effect. Aiming at this deficiency, it is proposed to use the Gaussian Mixed Model (GMM) to train nonlocal self-similar image patch of the external clean image in the local dictionary learning, and use the learned GMM to guide the internal image clustering; when performing the sparse coefficient estimation, the structural similarity index(SSIM) is introduced into the Euclidean distance and the improved Euclidean distance is introduced into the weight calculation. The experimental results show that the improved algorithm proposed in this paper has improved the PSNR and the visual effect of the image.