Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has oc
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Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has occurred is assumed unknown, while in non-blind de-blurring, this kernel is given. In blind de-blurring, the blurring kernel must be estimated in order to sharpen the corrupted image. This may increase the computational cost of the de-blurring process. Non-blind image de-blurring is an ill-posed problem with linear reverse issues. Therefore, we develop optimization problems in order to estimate the original sharp images. Usually, non-blind de-blurring methods assume that the blurring kernel is error-free, however, in practice our knowledge of the PSF is uncertain. Hence, in this paper, we use a semi-blind method for de-blurring the blurred image that is robust to this uncertainty. The proposed robust optimization model is followed by a filter for image de-blurring that can attain the solution with lowest possible error in the worst case scenarios, that is, the maximum uncertainty about the blurring kernel. Based on the simulation results, our proposed semi-blind model yields more than 4 dB PSNR improvements compared to conventional blind image de-blurring methods.
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