This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a $\ell_2$-norm data-term and an analysis sparse prior. The parameters of our network are the step-sizes of the Chambolle-Pock scheme and the linear operator involved in sparsity-based penalization, including implicitly the regularization parameter. A backpropagation procedure is fully described. Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database.
Zhengzhou University（郑州大学）；Univ Lyon, Ens de Lyon, Univ Lyon 1, CNRS, Laboratoire de Physique（里昂大学，里昂高等师范学院，里昂第一大学，法国国家科学研究中心，物理实验室）
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…
This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks. Considering that the sample number and internal dimension in score-based generative model have key influence on estimating the gradients of data distribution, two different high-dimensional ways are proposed: The channel-copy transformation increases the sample number and the pixel-scale transformation decreases feasible space dimension. Subsequently, a set of high-dimensional tensors represented by these transformations are used to t…
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur. First, we examine the weakness of conventional "fixated" models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks. The main idea is to exploit a feedback mechanism to s…
Tel-Aviv University（特拉维夫大学）；Ulsan National Institute of Science and Technology（蔚山国立科学技术院）；Weizmann Institute of Science（魏茨曼科学研究所）
Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. In recent years, solutions that are based on deep Convolutional Neural Networks (CNNs) have shown great promise. Yet, most of these techniques, which train CNNs using external data, are restricted to the observation models that have been used in the training phase. A recent alternative that does not have this drawback relies on learning the target image using internal learning. One such prominent example is the Deep Image Prior (DIP) technique that trains a network directly on the …
This paper presents a scalable approximate Bayesian method for image restoration using total variation (TV) priors. In contrast to most optimization methods based on maximum a posteriori estimation, we use the expectation propagation (EP) framework to approximate minimum mean squared error (MMSE) estimators and marginal (pixel-wise) variances, without resorting to Monte Carlo sampling. For the classical anisotropic TV-based prior, we also propose an iterative scheme to automatically adjust the regularization parameter via expectation-maximization (EM). Using Gaussian approximating densities wi…
Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor analysis for tensors generated by grouped image patches. The low-rank tensors are fed into the alternative direction multiplier method (ADMM) to further improve image reconstruction. The motivating application is compressive sensing (CS), and a deep convolutional architecture is adopted to approximate the expensive matrix inversion in CS applications. An iterative algorithm based on this low-rank tensor factorization s…
Since the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used in various image processing studies. Nonetheless, nuclear norm based convex surrogate of the rank function usually over-shrinks the rank components and makes different components equally, and thus may produce a result far from the optimum. To alleviate the above-mentioned limitations of the nuclear norm, in this paper we propose a new method for image restoration via the non-convex weighted Lp nuclear norm minimization (NCW-NNM), which is able to more …
Dual Prompting Image Restoration with Diffusion Transformers
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学网络科学与技术学院）；MoE Key Laboratory of Information Technology（信息科技联合实验室）；Huawei Noah’s Ark Lab（华为诺亚实验室）；The Chinese University of Hong Kong（香港中文大学）；Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
From Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC
organization= Institute of Pathology, Technical University of Munich, TUM School of Medicine；organization= Computer Aided Medical Procedures (CAMP), TU Munich , city= Munich , country= Germany；organization= Munich Center for Machine Learning (MCML) , city= Munich , country= Germany；organization= TUM School of Computation, Information；organization= Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences , city= Mashhad , country= Iran；organization= Munich Data Science Institute (MDSI) , city= Munich , country= Germany
Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）；Xi’an Jiaotong University（西安交通大学）；Nankai University（南开大学）；SenseTime Research（商汤科技研究院）
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''. Inspired by Functional theory, we provide several alternative solutions to implement our belief in the strict mathematical manifolds including Taylor's Unfolding Network, Invertible Neural Network, Central Difference Convolution and Zero-order Filtering as ``random weights network prototype'' with respect of the following f…
All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Science（中国科学院软件研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；National Research Center for Information Science and Technology (BNRist)（国家信息科学与技术研究中心（BNRist））；Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）
Numerous total variation (TV) regularizers, engaged in image restoration problem, encode the gradients by means of simple $[-1,1]$ FIR filter. Despite its low computational processing, this filter severely deviates signal's high frequency components pertinent to edge/discontinuous information and cause several deficiency issues known as texture and geometric loss. This paper addresses this problem by proposing an alternative model to the TV regularization problem via high order accuracy differential FIR filters to preserve rapid transitions in signal recovery. A numerical encoding scheme is de…
Robust Low-light Scene Restoration via Illumination Transition
The Hong Kong University of Science and Technology（香港科学与技术大学）；The Hong Kong Polytechnic University（香港理工大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Surrey（萨里大学）
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Inception Institute of AI（Inception人工智能研究院）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；University of California, Merced（加州大学默塞德分校）；Yonsei University（延世大学）；Google Research（谷歌研究院）
University of Oxford（牛津大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs. While supervised image reconstruction aims to minimize residual terms directly, RCL alternatively builds a connection between residuals and CL by defining a novel instance discrimination pretext task, using residuals as the discriminative feature. Our formul…
Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown degradations in real-world applications. Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation. However, their results still remain visible artifacts and detail distortion due to the estimation errors. To alleviate these problems, in this paper, we propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without b…
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is L2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses…
Nanyang Technological University（南洋理工大学）；Delft University of Technology（代尔夫特理工大学）
State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture search in image restoration, how it is connected to the classic deterministic training in solving inverse problems remains unclear. In this work, we propose a novel image denoising scheme via Residual Recovery using Reinforcement Learning, dubbed R3L. W…
From Fog Chamber to Aircraft Window: Pixel-Registered Imaging and Synthetic Fine-Tuning Enable Cross-Domain Defogging
Department of Electrical and Computer Engineering, University of Utah（犹他大学电气与计算机工程系）；U.S. Naval Research Laboratory Remote Sensing Division（美国海军研究实验室远程传感部门）
Samsung AI Center（三星人工智能中心）；University of Oxford（牛津大学）
In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low-resolution image in order to meet application-specific image quality demands and plays a key role in mobile devices. To comply with privacy regulations and reduce the overhead of cloud computing, executing SR models locally on-device constitutes a key alternative approach. Nevertheless, the excessive compute and memory requirements of SR workloads pose a challenge in mapping SR networks on resource-constrained mobil…
One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for hand-designed procedures. However, recent methods for single image super-resolution (SR) fail to maintain this advantage. They utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution (LR) image to the high resolution (HR) size with hand-designed techniques (e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image to reconstruct HR results. In this paper, we seek an alternative and p…
