SSIM Compliant Modeling Framework With Denoising and Deblurring Applications

In image processing, it is well known that mean square error criteria is perceptually inadequate. Consequently, image quality assessment (IQA) has emerged as a new branch to overcome this issue, and this has led to the discovery of one of the most popular perceptual measures, namely, the structural similarity index (SSIM). This measure is mathematically simple, yet powerful enough to express the quality of an image. Therefore, it is natural to deploy SSIM in model based applications, such as denoising, restoration, classification, etc. However, the non-convex nature of this measure makes this task difficult. Our attempt in this work is to discuss problems associated with its convex program and take remedial action in the process of obtaining a generalized convex framework. The obtained framework has been seen as a component of an alternative learning scheme for the case of a regularized linear model. Subsequently, we develop a relevant dictionary learning module as a part of alternative learning. This alternative learning scheme with sparsity prior is finally used in denoising and deblurring applications. To further boost the performance, an iterative scheme is developed based on the statistical nature of added noise. Experiments on image denoising and deblurring validate the effectiveness of the proposed scheme. Furthermore, it has been shown that the proposed framework achieves highly competitive performance with respect to other schemes in literature and performs better in natural images in terms of SSIM and visual inspection.