Comparing performance measures of sparse representation on image restoration algorithms

Image restoration is a systematic process that regains the lost clarity of an image. In the past, image restoration based on sparse representation has resulted in better performance for natural images. Within each category of image restoration such as de-blurring, de-noising and super resolution, different algorithms are selected for evaluation and comparison. It is evident that both local and non-local methods within each algorithm can produce improved image restoration results based on the over complete representations using learned dictionary. The Gaussian noise is added with the original image and comparative study is made from the three different de-noising techniques such as mean filter, Least Mean Square (LMS) adaptive filters and median filters. The experimental results arrived from the filters are discussed for each model of the selected image restoration algorithms-locally adaptive sparsity and regularization, Centralized Sparse Representation (CSR), low-rank approximation structured sparse representation and non-locally CSR. A comprehensive study of this paper would serve as a good reference and stimulate new research ideas in Image Restoration (IR).