非盲折叠是一个不良问题。大多数现有方法通常将该问题与最大-A-Bouthiori框架制定，并通过设计潜在清晰图像的类型的正则化术语和数据项来解决它。在本文中，我们通过学习鉴别性收缩函数来提出有效的非盲折叠方法来隐含地模拟这些术语。与使用深度卷积神经网络（CNNS）或径向基函数的大多数现有方法来说，我们简单地学习正则化术语，我们制定数据项和正则化术语，并将解构模型分成与数据相关和正则化相关的子 - 根据乘法器的交替方向方法问题。我们探讨了Maxout函数的属性，并使用颤扬层开发一个深入的CNN模型，以学习直接近似对这两个子问题的解决方案的判别缩小功能。此外，考虑到基于快速的傅里叶变换的图像恢复通常导致振铃伪像，而基于共轭梯度的图像恢复是耗时的，我们开发共轭梯度网络以有效且有效地恢复潜在的清晰图像。实验结果表明，该方法在效率和准确性方面对最先进的方法有利地执行。

盲图修复（IR）是计算机视觉中常见但充满挑战的问题。基于经典模型的方法和最新的深度学习（DL）方法代表了有关此问题的两种不同方法，每种方法都有自己的优点和缺点。在本文中，我们提出了一种新颖的盲图恢复方法，旨在整合它们的两种优势。具体而言，我们为盲IR构建了一个普通的贝叶斯生成模型，该模型明确描绘了降解过程。在此提出的模型中，PICEL的非I.I.D。高斯分布用于适合图像噪声。它的灵活性比简单的I.I.D。在大多数常规方法中采用的高斯或拉普拉斯分布，以处理图像降解中包含的更复杂的噪声类型。为了解决该模型，我们设计了一个变异推理算法，其中所有预期的后验分布都被参数化为深神经网络，以提高其模型能力。值得注意的是，这种推论算法诱导统一的框架共同处理退化估计和图像恢复的任务。此外，利用了前一种任务中估计的降解信息来指导后一种红外过程。对两项典型的盲型IR任务进行实验，即图像降解和超分辨率，表明所提出的方法比当前最新的方法实现了卓越的性能。

尽管目前基于深度学习的方法在盲目的单图像超分辨率（SISR）任务中已获得了有希望的表现，但其中大多数主要集中在启发式上构建多样化的网络体系结构，并更少强调对Blur之间的物理发电机制的明确嵌入内核和高分辨率（HR）图像。为了减轻这个问题，我们提出了一个模型驱动的深神经网络，称为blind SISR。具体而言，为了解决经典的SISR模型，我们提出了一种简单的效果迭代算法。然后，通过将所涉及的迭代步骤展开到相应的网络模块中，我们自然构建了KXNET。所提出的KXNET的主要特异性是整个学习过程与此SISR任务的固有物理机制完全合理地集成在一起。因此，学习的模糊内核具有清晰的物理模式，并且模糊内核和HR图像之间的相互迭代过程可以很好地指导KXNET沿正确的方向发展。关于合成和真实数据的广泛实验很好地证明了我们方法的卓越准确性和一般性超出了当前代表性的最先进的盲目SISR方法。代码可在：\ url {https://github.com/jiahong-fu/kxnet}中获得。

在这项工作中，我们研究了非盲目图像解卷积的问题，并提出了一种新的经常性网络架构，其导致高图像质量的竞争性恢复结果。通过现有大规模线性求解器的计算效率和稳健性的推动，我们设法将该问题的解决方案表达为一系列自适应非负数最小二乘问题的解决方案。这引发了我们提出的复发性最小二乘因解网络（RLSDN）架构，其包括在其输入和输出之间施加线性约束的隐式层。通过设计，我们的网络管理以同时服务两个重要的目的。首先，它隐含地模拟了可以充分表征这组自然图像的有效图像，而第二种是它恢复相应的最大后验（MAP）估计。近期最先进的方法的公开数据集的实验表明，我们提出的RLSDN方法可以实现所有测试方案的灰度和彩色图像的最佳报告性能。此外，我们介绍了一种新颖的培训策略，可以通过任何网络架构采用，这些架构涉及线性系统作为其管道的一部分的解决方案。我们的策略完全消除了线性求解器所需迭代的需要，因此，它在训练期间显着降低了内存占用。因此，这使得能够培训更深的网络架构，这可以进一步提高重建结果。

盲目解构是一种在各种田地中产生的不良问题，从显微镜到天文学。问题的不良性质需要足够的前沿到达理想的解决方案。最近，已经表明，深度学习架构可以用作在无监督盲卷积优化期间的图像生成，然而甚至在单个图像上也呈现性能波动。我们建议使用Wiener-Deconvolulation在优化期间通过从高斯开始使用辅助内核估计来指导图像发生器在优化期间。我们观察到与低频特征相比，通过延迟再现去卷积的高频伪影。另外，图像发生器从模糊图像的速度再现解码图像的低频特征。我们在约束的优化框架中嵌入计算过程，并表明该方法在多个数据集中产生更高的稳定性和性能。此外，我们提供代码。

使用注意机制的深度卷积神经网络（CNN）在动态场景中取得了巨大的成功。在大多数这些网络中，只能通过注意图精炼的功能传递到下一层，并且不同层的注意力图彼此分开，这并不能充分利用来自CNN中不同层的注意信息。为了解决这个问题，我们引入了一种新的连续跨层注意传播（CCLAT）机制，该机制可以利用所有卷积层的分层注意信息。基于CCLAT机制，我们使用非常简单的注意模块来构建一个新型残留的密集注意融合块（RDAFB）。在RDAFB中，从上述RDAFB的输出中推断出的注意图和每一层直接连接到后续的映射，从而导致CRLAT机制。以RDAFB为基础，我们为动态场景Deblurring设计了一个名为RDAFNET的有效体系结构。基准数据集上的实验表明，所提出的模型的表现优于最先进的脱毛方法，并证明了CCLAT机制的有效性。源代码可在以下网址提供：https：//github.com/xjmz6/rdafnet。

本文提出了图像恢复的新变异推理框架和一个卷积神经网络（CNN）结构，该结构可以解决所提出的框架所描述的恢复问题。较早的基于CNN的图像恢复方法主要集中在网络体系结构设计或培训策略上，具有非盲方案，其中已知或假定降解模型。为了更接近现实世界的应用程序，CNN还接受了整个数据集的盲目培训，包括各种降解。然而，给定有多样化的图像的高质量图像的条件分布太复杂了，无法通过单个CNN学习。因此，也有一些方法可以提供其他先验信息来培训CNN。与以前的方法不同，我们更多地专注于基于贝叶斯观点以及如何重新重新重构目标的恢复目标。具体而言，我们的方法放松了原始的后推理问题，以更好地管理子问题，因此表现得像分裂和互动方案。结果，与以前的框架相比，提出的框架提高了几个恢复问题的性能。具体而言，我们的方法在高斯denoising，现实世界中的降噪，盲图超级分辨率和JPEG压缩伪像减少方面提供了最先进的性能。

散焦模糊是图像中经常看到的一种模糊效果，这是由于其空间变体的量而挑战。本文介绍了一种用于从单个图像中移除散焦模糊的端到端深度学习方法，以便具有随之而来的视觉任务的全焦点图像。首先，提出了一种用于以有效的线性参数形式表示空间变体散焦模糊核的像素 - WISE高斯核混合物（GKM）模型，其比现有模型更高。然后，通过展开基于GKM的去纹理的定点迭代来开发称为GKMNet的深神经网络。 GKMNET构建在轻量级刻度 - 重复间体系结构上，具有比例 - 复制注意力模块，用于估计GKM中的混合系数用于散焦去孔。广泛的实验表明，GKMNET不仅明显优于现有的散焦去纹理方法，而且还具有其在模型复杂性和计算效率方面的优势。

As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a RCD model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis on what happens inside the network and why it works well in inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to better deraining performance. Comprehensive experiments substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules. Code is available in \emph{\url{https://github.com/hongwang01/DRCDNet}}.

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise (AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

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 blur kernel prior incorporation for blind SR. Specifically, in our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. The DSMM consists of two components: a detail restoration unit (DRU) and a structure modulation unit (SMU). The former aims at regressing the intermediate HR detail reconstruction from LR structural contexts, and the latter performs structural contexts modulation conditioned on the learned detail maps at both HR and LR spaces. Besides, we use the output of DSMM as the hidden state and design our DSSR architecture from a recurrent convolutional neural network (RCNN) view. In this way, the network can alternatively optimize the image details and structural contexts, achieving co-optimization across time. Moreover, equipped with the recurrent connection, our DSSR allows low- and high-level feature representations complementary by observing previous HR details and contexts at every unrolling time. Extensive experiments on synthetic datasets and real-world images demonstrate that our method achieves the state-of-the-art against existing methods. The source code can be found at https://github.com/Arcananana/DSSR.

在弱光环境下，手持式摄影在长时间的曝光设置下遭受了严重的相机震动。尽管现有的Deblurry算法在暴露良好的模糊图像上表现出了令人鼓舞的性能，但它们仍然无法应对低光快照。在实用的低光脱毛中，复杂的噪声和饱和区是两个主导挑战。在这项工作中，我们提出了一种称为图像的新型非盲脱毛方法，并具有特征空间Wiener Deonervolution网络（Infwide），以系统地解决这些问题。在算法设计方面，Infwide提出了一个两分支的架构，该体系结构明确消除了噪声并幻觉，使图像空间中的饱和区域抑制了特征空间中的响起文物，并将两个互补输出与一个微妙的多尺度融合网络集成在一起高质量的夜间照片浮雕。为了进行有效的网络培训，我们设计了一组损失功能，集成了前向成像模型和向后重建，以形成近环的正则化，以确保深神经网络的良好收敛性。此外，为了优化Infwide在实际弱光条件下的适用性，采用基于物理过程的低光噪声模型来合成现实的嘈杂夜间照片进行模型训练。利用传统的Wiener Deonervolution算法的身体驱动的特征并引起了深层神经网络的表示能力，Infwide可以恢复细节，同时抑制在脱毛期间的不愉快的人工制品。关于合成数据和实际数据的广泛实验证明了所提出的方法的出色性能。

Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a twofaceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://github.com/swz30/MPRNet.

Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to nonblindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.

Defocus Blur是大多数相机中使用的光学传感器的物理后果。尽管它可以用作摄影风格，但通常被视为图像降解，以形成模型的尖锐图像，并具有空间变化的模糊内核。在过去几年的模糊估计方法的推动下，我们提出了一种非盲方法来处理图像脱毛的方法，可以处理空间变化的核。我们介绍了两个编码器子网络网络，它们分别用模糊图像和估计的模糊图，并作为输出作为输出（Deconvolved）图像的输出。每个子网络都会呈现几个跳过连接，这些连接允许分开分开的数据传播，还可以通过划线跳过连接，以简化模块之间的通信。该网络经过合成的模糊内核训练，这些核被增强以模拟现有模糊估计方法产生的模糊图，我们的实验结果表明，当与多种模糊估计方法结合使用时，我们的方法很好地工作。

在本文中，我们考虑了Defocus图像去缩合中的问题。以前的经典方法遵循两步方法，即首次散焦映射估计，然后是非盲目脱毛。在深度学习时代，一些研究人员试图解决CNN的这两个问题。但是，代表模糊级别的Defocus图的简单串联导致了次优性能。考虑到Defocus Blur的空间变体特性和Defocus Map中指示的模糊级别，我们采用Defocus Map作为条件指导来调整输入模糊图像而不是简单串联的特征。然后，我们提出了一个基于Defocus图的空间调制的简单但有效的网络。为了实现这一目标，我们设计了一个由三个子网络组成的网络，包括DeFocus Map估计网络，该网络将DeFocus Map编码为条件特征的条件网络以及根据条件功能执行空间动态调制的DeFocus Deblurring网络。此外，空间动态调制基于仿射变换函数，以调整输入模糊图像的特征。实验结果表明，与常用的公共测试数据集中的现有最新方法相比，我们的方法可以实现更好的定量和定性评估性能。

Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and real-world images show that the proposed method achieves the state-of-the-art performance compared to existing methods.

在各种基于学习的图像恢复任务（例如图像降解和图像超分辨率）中，降解表示形式被广泛用于建模降解过程并处理复杂的降解模式。但是，在基于学习的图像deblurring中，它们的探索程度较低，因为在现实世界中挑战性的情况下，模糊内核估计不能很好地表现。我们认为，对于图像降低的降解表示形式是特别必要的，因为模糊模式通常显示出比噪声模式或高频纹理更大的变化。在本文中，我们提出了一个框架来学习模糊图像的空间自适应降解表示。提出了一种新颖的联合图像re毁和脱蓝色的学习过程，以提高降解表示的表现力。为了使学习的降解表示有效地启动和降解，我们提出了一个多尺度退化注入网络（MSDI-NET），以将它们集成到神经网络中。通过集成，MSDI-NET可以适应各种复杂的模糊模式。 GoPro和Realblur数据集上的实验表明，我们提出的具有学识渊博的退化表示形式的Deblurring框架优于最先进的方法，具有吸引人的改进。该代码在https://github.com/dasongli1/learning_degradation上发布。

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 not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.

派生是一个重要而基本的计算机视觉任务，旨在消除在下雨天捕获的图像或视频中的雨条纹和累积。现有的派威方法通常会使雨水模型的启发式假设，这迫使它们采用复杂的优化或迭代细化以获得高回收质量。然而，这导致耗时的方法，并影响解决从假设偏离的雨水模式的有效性。在本文中，我们通过在没有复杂的雨水模型假设的情况下，通过在没有复杂的雨水模型假设的情况下制定污染作为预测滤波问题的简单而有效的污染方法。具体地，我们识别通过深网络自适应地预测适当的核的空间变型预测滤波（SPFILT以过滤不同的各个像素。由于滤波可以通过加速卷积来实现，因此我们的方法可以显着效率。我们进一步提出了eFderain +，其中包含三个主要贡献来解决残留的雨迹，多尺度和多样化的雨水模式而不会损害效率。首先，我们提出了不确定感知的级联预测滤波（UC-PFILT），其可以通过预测的内核来识别重建清洁像素的困难，并有效地移除残留的雨水迹线。其次，我们设计重量共享多尺度扩张过滤（WS-MS-DFILT），以处理多尺度雨条纹，而不会损害效率。第三，消除各种雨水模式的差距，我们提出了一种新颖的数据增强方法（即Rainmix）来培养我们的深层模型。通过对不同变体的复杂分析的所有贡献相结合，我们的最终方法在恢复质量和速度方面优于四个单像辐照数据集和一个视频派威数据集的基线方法。