摘要： We consider the image recovery problem where the observed image is simultaneously corrupted by blur and impulse noise. Due to the edge preserving property of the total variation, and the property of providing good sparse approximation to piecewise smooth functions of the wavelet frames, we propose a Framelet-based hybrid regularization model to significantly lessen staircase artifacts while well preserving the valuable edge information of the image. We take advantage of an alternating direction method of multiplier to efficiently find a solution of this model. Because of the convex of our model, the convergence of our method can be guaranteed. Experimental results are finally presented to show the efficiency of our method in terms of the peak-signal-to-noise ratio, structure similarity index measure and the relative error.
关键词：total variation; impulse noise; alternating direction method of multiplier; Framelet.
