Learning Deep CNN Denoiser Prior for Image Restoration

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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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Grayscale Image Denoising BSD68 sigma15 Deep CNN Denoiser PSNR 31.63 # 5
Color Image Denoising BSD68 sigma15 Deep CNN Denoiser PSNR 33.86 # 2
Grayscale Image Denoising BSD68 sigma25 Deep CNN Denoiser PSNR 29.15 # 7
Color Image Denoising BSD68 sigma25 Deep CNN Denoiser PSNR 31.16 # 3
Color Image Denoising BSD68 sigma35 Deep CNN Denoiser PSNR 29.5 # 1
Color Image Denoising BSD68 sigma5 Deep CNN Denoiser PSNR 40.36 # 1
Grayscale Image Denoising BSD68 sigma50 Deep CNN Denoiser PSNR 26.19 # 9
Color Image Denoising CBSD68 sigma50 IRCNN PSNR 27.86 # 6
Image Super-Resolution Set14 - 2x upscaling Deep CNN Denoiser PSNR 30.79 # 18
Image Super-Resolution Set14 - 3x upscaling Deep CNN Denoiser PSNR 27.72 # 11
Image Super-Resolution Set14 - 4x upscaling Deep CNN Denoiser PSNR 27.59 # 42
Image Super-Resolution Set5 - 2x upscaling Deep CNN Denoiser PSNR 35.05 # 22
Image Super-Resolution Set5 - 3x upscaling Deep CNN Denoiser PSNR 31.26 # 15
Image Super-Resolution Set5 - 4x upscaling Deep CNN Denoiser PSNR 30.92 # 35

Methods used in the Paper


METHOD TYPE
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