Abstract:

In order to alleviate the defects in image restoration, e.g., the damage of the edges and the loss of the details, a new gradient sparsity regularization model is derived based on the analysis of the gradient histogram and the best penalty in sparse representation. The proposed model can not only highlight the image detail effectively but also achieve a good balance between blur and noise removal and edge preservation. A new optimization algorithm is designed to solve the new model. Simulation experiments on image denoising and deblurring confirm that the numerical method is fast and efficient, the proposed regularization model can well preserve the significant edges and textures when effectively removing the blur and noise.