Blind image restoration method regularized by hybrid gradient sparse prior

Overview: Blind image restoration is widely used to improve the quality of degraded images with no-reference. Its main purpose is to accurately estimate the blur kernel and the wanted clear image. In the blind restoration research based on high-order image gradient priors, most of the existing works employ the total variation to model the gradient prior constraints. This class of method can effectively suppress the blocking artifact in the restored image. On the basis of experimental observation and research, this paper proposes to use the sparse prior constraint model to regularize the blind restoration process to obtain a better image restoration performance. On the one hand, by looking into the histogram of high-order and low-order gradients of a natural image, it can be found that the sparsity of high-order gradients is even more salient than that of low-order gradients. On the other hand, the existing researches show that the use of sparse priors to describe a heavy-tailed distribution of low-order gradients helps to restore the significant edges of the have image while effectively suppressing noise and ringing effects. Therefore, this work proposes to combine the low-order and high-order gradient priors into a new sparse regularization term so as to benefit from both types of gradient priors. In the meantime, an interesting experimental finding is introduced in this work that different degrees of image blur favor different ratios of the two types of gradient priors which are beneficial to obtain the optimal solution. Therefore, to obtain better iteration convergence, an adaptive factor ω based on image entropy is introduced to adjust the ratio of the two types of gradient priors in the iterative optimization process. Since it is hard to model the parameter ωmathematically, the expression of ω is determined by manually parameter adjustment and statistically data fitting in this work. The overall iterative optimization process is developed in a coarse-to-fine manner, and the split Bergman method is employed to deal with the non-convex problem of each minimization subtask. Finally, to analyze the performance of the proposed blind image restoration method, the ablation study was firstly conducted to demonstrate the efficiency of the employed strategies. Then, on the BSDS image dataset (simulated dataset) and the GOPRO image dataset (real blurred dataset), the proposed method was compared with the existing state-of-the-art image restoration methods. Experimental results show that our method can recover sharper edges and smoother details as well as introduce less unwanted artifacts, and our method is at a relatively leading level not only in subjective visual effects but also in performance evaluation indicators. All the above advantages demonstrate that the proposed method has superior image restoration performance.