Sparse based representation is being used extensively for image restoration. The dictionary learningthrough patch extraction is central to the sparse based schemes. In the process of dictionary learning,a large number of patches will be extracted from high quality images and dictionary will be formed.Hence, over-complete dictionaries will be built. To overcome the complexity associated with overcompletedictionaries many schemes were proposed. Of them, the adaptive sparse domain is thepopular one. Many variations of adaptive sparse domain schemes were proposed. Selection of obviouspatches is common to all. In all these schemes, individual patches will be considered as the basic entityand will be used. This is the reason for the complexity involved in sparse representation. In this paper,to avoid the complexity, the patches are grouped according to the similarity among the patches. Inaddition to reduce the complexity the proposed cluster based scheme considers the self-similarity ofthe patches involved. Hence better performance with less complexity is possible with the proposedschemes. In the process of testing, in addition to uniform blur and Gaussian blur, a combination of thetwo blurs is also considered.
Uniform and Non-Uniform Single Image Deblurring Based on Sparse Representation and Adaptive Dictionary Learning