Papers › Deep Mean-Shift Priors for Image Restoration
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro, Matthias Zwicker
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | Set14 - 4x upscaling | Deep Mean-Shift Priors | PSNR | 26.22 | #98 of 104 | Archive leaderboard | report |
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