Plug-and-Play Image Restoration with Deep Denoiser Prior

• URL: http://arxiv.org/abs/2008.13751v2
• Date: Mon, 12 Jul 2021 20:28:19 GMT
• Title: Plug-and-Play Image Restoration with Deep Denoiser Prior
• Authors: Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte
• Abstract summary: We show that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. We set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems.
• Score: 186.84724418955054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR.

Related papers

• Deep Learning CT Image Restoration using System Blur and Noise Models [2.2530496464901106]
  We present a method that leverages both degraded image inputs and a characterization of the system blur and noise to combine modeling and deep learning approaches. Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics.
  arXiv  Detail & Related papers  (2024-07-20T21:17:35Z)
• Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning [3.632876183725243]
  inverse halftoning is a classic image restoration task, aiming to recover continuous-tone images from halftone images with only bilevel pixels. We propose an end-to-end multiscale progressively residual learning network (MSPRL), which has a UNet architecture and takes multiscale input images.
  arXiv  Detail & Related papers  (2023-05-27T03:37:33Z)
• Denoising Diffusion Models for Plug-and-Play Image Restoration [135.6359475784627]
  This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models.
  arXiv  Detail & Related papers  (2023-05-15T20:24:38Z)
• Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework [16.579397398441102]
  We design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal. Our framework outcompetes the state-of-the-art methods in both image enhancement and denoising.
  arXiv  Detail & Related papers  (2022-10-11T13:29:10Z)
• Variational Deep Image Restoration [20.195082841065947]
  This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction.
  arXiv  Detail & Related papers  (2022-07-03T16:32:15Z)
• Learning Discriminative Shrinkage Deep Networks for Image Deconvolution [122.79108159874426]
  We propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy.
  arXiv  Detail & Related papers  (2021-11-27T12:12:57Z)
• Image Restoration by Deep Projected GSURE [115.57142046076164]
  Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
  arXiv  Detail & Related papers  (2021-02-04T08:52:46Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
  We first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
  arXiv  Detail & Related papers  (2020-02-10T13:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.