Learning Deep CNN Denoiser Prior for Image Restoration

CVPR 2017 Kai ZhangWangmeng ZuoShuhang GuLei Zhang

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Image Denoising BSD68 sigma15 Deep CNN Denoiser PSNR 31.63 # 4
Image Denoising BSD68 sigma25 Deep CNN Denoiser PSNR 29.15 # 5
Image Denoising BSD68 sigma50 Deep CNN Denoiser PSNR 26.19 # 7