Dual dictionary sparse restoration of blurred images

An image restoration method based on a dual dictionary was presented under the framework of sparse theory, and the choice of overcomplete dictionaries and the implementation of iteration methods were analyzed. Firstly, the degradation and the restoration models in the sparse theory were established, then the dictionary constructed by Haar coefficients was used to sparse the blurred image and shrink the image with Parallel Coordinate Decent(PCD) iteration algorithm to obtain the elementary deblurred image, in which the blur was removed efficiently, but the noise was weighted and added. For removing the weighted noise, the secondary dictionary from an image database was trained to shrink the deblurred image and get the final result. The results shows that the proposed method can restore the motion-blurred image efficiently, remove motion blur and noise and reserve the edge detail in some extents. Finally the two-level sparse optimization model was expanded and a new idea for the image restoration was presented under the sparse framework.