SSIM-based non-local means image denoising

Perceptually inspired image processing has been an emerging field of study in recent years. Here we make one of the first efforts to incorporate the structural similarity (SSIM) index, a successful perceptual image quality assessment measure, into the framework of non-local means (NLM) image denois-ing, which is a state-of-the-art method that delivers superior desnoising performance. Specifically, a denoised image patch is obtained by weighted averaging of neighboring patches, where the similarity between patches as well as the weights assigned to the patches are determined based on an estimation of SSIM. A two-stage approach is proposed for robust SSIM estimation in the presence of noise. Moreover, motivated by the ideas behind SSIM, we adjust the contrast and mean of each patch before feeding it into the weighted averaging pro-cess. Our experimental results show that the proposed SSIM-based NLM algorithm achieves better SSIM and PSNR per-formance and provides better visual quality than least square based NLM method. Index Terms — image denoising, structural similarity, non-local means, perceptual image processing 1