Abstract

This work highlights the use of group-based sparse representation strategy to improve picture quality from a degraded one, which outperforms the previous method. Various picture restoration challenges, such as different forms of noise and blurring functions, have prompted the development of several advanced approaches. Parameters affect the efficacy of each strategy differently. The ability to recognize altered pictures, pinpoint their location, and restore them is a must for contemporary communication systems and multimedia. The extraction of a clean, original picture from a blurry one using a degradation and restoration model is known as image restoration, and it becomes a major concern in high-level image processing. Typically, as an image is being acquired, it gets degraded. The term "image restoration" refers to a set of procedures that may be used to restore damaged pictures back to their original state, whether the user is aware of the degradation process or not. Denoising, pict