Lp-Method-Noise Based Regularization Model for Image Restoration

Various regularization techniques have been sufficiently developed to improve the quality of the image restoration. By utilizing existing image smoothing operators, method noise provides a new way to formulate regularization functions. The so-called method noise refers to the difference of an image and its smoothed version, obtained by an image smoothing operator. It is concluded that the method noise of a clear image mainly contains edges and small scaled details, and should be as sparse as possible. Based on this conclusion, we introduce Lp-norm penalty on the method noise, which can accurately describe its sparse prior distribution. We formulate an L_p-method-noise based regularization model and analyze its advantages in terms of its solution and performance in image restoration. Specifically, the L_p-norm penalty of the method noise is better than other forms of norm in removing noise and keeping the details. Moreover, a modified Bregmanized operator splitting algorithm is designed for the proposed model. Experimental results show that the proposed method can obtain better results than other method noise based regularization methods.