A blind image deblurring algorithm based on relative gradient and sparse representation

A blind image deblurring algorithm based on relative gradient and sparse representation is proposed in this paper. The layered method restores the image by three steps: edge extraction, blur kernel estimation and image reconstruction. The positive and negative gradients in texture part have reversal changes, and the edge part that reflects the image structure has only one gradient change. According to the characteristic, the edge of the image is extracted by using the relative gradient of image, so as to estimate the blur kernel of the image. In the stage of image reconstruction, in order to overcome the problem of oversize of the image and the overcomplete dictionary matrix, the image is divided into small blocks. An overcomplete dictionary is used for sparse representation, and the image is reconstructed by the iterative threshold shrinkage method to improve the quality of image restoration. Experimental results show that the proposed method can effectively improve the quality of image restoration.