| Summary: | 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<sub>p</sub>-method-noise based regularization model and analyze its advantages in terms of its solution and performance in image restoration. Specifically, the L<sub>p</sub>-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.
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