Learning Deep CNN Denoiser Prior for Image Restoration

By Kai Zhang, Wangmeng Zuo, Shuhang Gu, Lei Zhang. 2017

2,119 citations recorded by the source corpus.

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. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, methodology): “As mentioned in the introduction, existing DNNs-based SISR methods are generally based on bicubic downsam-pling [23, 44] and traditional degradations [26, 37, 54, 59, 60], or some simple variants [11, 41, 53, 56, 58].”
GAN Inversion: A Survey (2021, background): “There are also other deep priors developed for low-level restoration tasks like deep denoiser prior [201], [202] and TNRD [203].”
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Image Super-Resolution Using Very Deep Residual Channel Attention Networks (2018, methodology): “We further apply our method to super-resolve images with blur-down (BD) degradation model, which is also commonly used recently [11,15,17].”
Pre-Trained Image Processing Transformer (2020, neutral): “…of the proposed method, Urban100( × 4): img 004 HR VDSR[41] EDSR[51] RDN[94] OISR[35] SAN[17] RNAN [93] IGNN BSD68: 163085 GT Noisy ( σ =50) CBM3D [16] TNRD [14] RDN [94] DnCNN [87] MemNet [65] IRCNN [88] FFDNet [89] IPT (ours) we compare our results with various state-of-the-art models.”
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Image De-Raining Transformer (2022, neutral): “In recent years, deep learning are widely used in various image processing tasks [17], [18], [31], [32], [33], [34], [35], such as de-noising [36], [37], [38], super-resolution [39], [40], [41], de-blurring [42], [43], [44], [45], de-hazing [19], [19], [46], [47], [48], [49], [50], low-light image…”
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