Image deblurring based on noise characteristics and l_1 convex relaxation
Shi Guangming · Chinese Journal of Stereology and Image Analysis · 2011
Image deblurring is inherently to solve an ill-posed problem.The technique of l1convex relaxation is usually used to solve this ill-posed problem because any one image exists a sparse domain in theory.However,different types of noise may be introduced into the blur image in the practical process of acquiring the image.It is difficult to obtain satisfactory results if the same model is used to deblur the image for different degrees.In view of this,we firstly analyze the characteristics which the blur image is corrupted by noise,and then propose that different models of l1 convex relaxation are used to recover the image.In the proposed method,different fidelity terms are used in the optimization models of l1 convex relaxation according to all pixels or parts of the blur image corrupted by the noise.The experimental results verify the correctness and efficiency of the proposed method.