Deep Mean-Shift Priors for Image Restoration

In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization. We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Super-Resolution Set14 - 4x upscaling Deep Mean-Shift Priors PSNR 26.22 # 59
Image Super-Resolution Set5 - 4x upscaling Deep Mean-Shift Priors PSNR 29.16 # 67


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