SSIM-inspired image denoising using sparse representations

Perceptual image quality assessment (IQA) and sparse signal representation have recently emerged as high-impact research topics in the field of image processing. Here we make one of the first attempts to incorporate the structural similarity (SSIM) index, a promising IQA measure, into the framework of optimal sparse signal representation and approximation. In particular, we introduce a novel image denoising scheme where a modified orthogonal matching pursuit algorithm is proposed for finding the best sparse coefficient vector in maximum-SSIM sense for a given set of linearly independent atoms. Furthermore, a gradient descent algorithm is developed to achieve SSIM-optimal compromise in combining the input and sparse dictionary reconstructed images. Our experimental results show that the proposed method achieves better SSIM performance and provide better visual quality than least square optimal denoising methods.