Cartoon-texture composite regularization based non-blind deblurring method for partly-textured blurred images with Poisson noise

• Our method utilizes different regularization terms for cartoon and texture components.
• Our method can eliminate the staircase effect without distinguishing the regions of image.
• Our method can eliminate the artifact in non-textured region caused by the nonlocal TV.
• Our ADMM based algorithm can solve the new model efficiently.
• Our method can improve subjective vision, PSNR and SSIM efficiently.

In recent years, deblurring image with Poisson noise has attracted more and more attention in many areas such as astronomy and biological imaging. This is an ill-posed problem and can be regularized to improve the quality of the solution. Fractional-order total variation regularization can eliminate the staircase effect caused by the total variation regularization and avoid over-smoothing at the edges caused by the high-order total variation regularization, but it cannot preserve textures well. Non-local regularization can preserve textures well but often causes extra artifacts especially in the non-texture regions. In this paper, we deal with the cartoon component and the texture component differently and propose a cartoon-texture composite regularization based non-blind deblurring method. We utilize fractional-order total variation regularization for the cartoon component to eliminate the staircase effect and avoid over-smoothing at the edges, and use the non-local total variation regularization for the texture component to preserve the details and eliminate the artifacts in the non-textured regions. We develop an alternating direction method of multipliers based algorithm to solve the proposed model. Experiments results show that our method can improve the result both visually and in terms of the peak signal to noise ratio and structural similarity index efficiently.

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