A deep primal-dual proximal network for image restoration

• URL: http://arxiv.org/abs/2007.00959v3
• Date: Mon, 20 Dec 2021 14:12:15 GMT
• Title: A deep primal-dual proximal network for image restoration
• Authors: Mingyuan Jiu, Nelly Pustelnik
• Abstract summary: We design a deep network, named DeepPDNet, built from primal-dual iterations associated with the minimization of a standard penalized likelihood with an analysis prior. Two different learning strategies: "Full learning" and "Partial learning" are proposed, the first one is the most efficient numerically. Extensive results show that the proposed DeepPDNet demonstrates excellent performance on the MNIST and the more complex BSD68, BSD100, and SET14 datasets for image restoration and single image super-resolution task.
• Score: 8.797434238081372
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image restoration remains a challenging task in image processing. Numerous methods tackle this problem, often solved by minimizing a non-smooth penalized co-log-likelihood function. Although the solution is easily interpretable with theoretic guarantees, its estimation relies on an optimization process that can take time. Considering the research effort in deep learning for image classification and segmentation, this class of methods offers a serious alternative to perform image restoration but stays challenging to solve inverse problems. In this work, we design a deep network, named DeepPDNet, built from primal-dual proximal iterations associated with the minimization of a standard penalized likelihood with an analysis prior, allowing us to take advantage of both worlds. We reformulate a specific instance of the Condat-Vu primal-dual hybrid gradient (PDHG) algorithm as a deep network with fixed layers. The learned parameters are both the PDHG algorithm step-sizes and the analysis linear operator involved in the penalization (including the regularization parameter). These parameters are allowed to vary from a layer to another one. Two different learning strategies: "Full learning" and "Partial learning" are proposed, the first one is the most efficient numerically while the second one relies on standard constraints ensuring convergence in the standard PDHG iterations. Moreover, global and local sparse analysis prior are studied to seek a better feature representation. We apply the proposed methods to image restoration on the MNIST and BSD68 datasets and to single image super-resolution on the BSD100 and SET14 datasets. Extensive results show that the proposed DeepPDNet demonstrates excellent performance on the MNIST and the more complex BSD68, BSD100, and SET14 datasets for image restoration and single image super-resolution task.

Related papers

• PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval [6.982256124089]
  Phase retrieval is a challenge nonlinear inverse problem in computational imaging and image processing. We have developed PRISTA-Net, a deep unfolding network based on the first-order iterative threshold threshold algorithm (ISTA) All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold, and step size, are learned-to-end instead of being set.
  arXiv  Detail & Related papers  (2023-09-08T07:37:15Z)
• Deep Generalized Unfolding Networks for Image Restoration [16.943609020362395]
  We propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. We integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm. Our method is superior in terms of state-of-the-art performance, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2022-04-28T08:39:39Z)
• Learning Weighting Map for Bit-Depth Expansion within a Rational Range [64.15915577164894]
  Bit-depth expansion (BDE) is one of the emerging technologies to display high bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods have no unified solution for various BDE situations. We design a bit restoration network (BRNet) to learn a weight for each pixel, which indicates the ratio of the replenished value within a rational range.
  arXiv  Detail & Related papers  (2022-04-26T02:27:39Z)
• DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution [15.986942312624]
  We study the problem of non-blind image deconvolution. We propose a novel recurrent network architecture that leads to very competitive restoration results of high image quality.
  arXiv  Detail & Related papers  (2021-12-10T13:16:51Z)
• Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images [42.26124628784883]
  We propose a compact, efficient, and end-to-end learning-based framework, namely AGD-Net. We first formulate the problem explicitly based on the classic gradient descent algorithm. AGD-Net can improve the reconstruction quality by more than 1.0 dB on average.
  arXiv  Detail & Related papers  (2021-08-12T05:54:09Z)
• LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond [75.37541439447314]
  Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. This paper proposes a linearly-assembled pixel-adaptive regression network (LAPAR) to strike a sweet spot of deep model complexity and resulting SISR quality.
  arXiv  Detail & Related papers  (2021-05-21T15:47:18Z)
• 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)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• 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)
• BP-DIP: A Backprojection based Deep Image Prior [49.375539602228415]
  We propose two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the degraded image; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.
  arXiv  Detail & Related papers  (2020-03-11T17:09:12Z)

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.