Deep Mean-Shift Priors for Image Restoration

2017/09/12 by Siavash Bigdeli, Meiguang Jin, Bigdeli, Siavash Arjomand +5 · 8 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1709.03749

openalex publication_date 2017/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Learning Deep CNN Denoiser Prior for Image Restoration
• The Little Engine that Could: Regularization by Denoising (RED)

Cited by

• Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
• Deep Learning on Image Denoising: An overview
• Learning to Optimize: A Primer and A Benchmark
• Disentangling Factors of Variation by Mixing Them
• Non-Local ConvLSTM for Video Compression Artifact Reduction
• MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
• MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior
• Infusing Learned Priors into Model-Based Multispectral Imaging

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