尽管通过卷积神经网络实现的光场超分辨率（LFSR）的最近进展，但由于4D LF数据的复杂性，灯场（LF）图像的相关信息尚未充分研究和利用。为了应对这种高维LF数据，大多数现有的LFSR方法采用将其分解成较低的尺寸并随后在分解的子空间上执行优化。然而，这些方法本质上是有限的，因为它们被忽略了分解操作的特性，并且仅利用了一组限量的LF子空间，最终未能全面提取时空角度并导致性能瓶颈。为了克服这些限制，在本文中，我们彻底发现了LF分解的潜力，并提出了一种新颖的分解核的概念。特别地，我们系统地将各种子空间的分解操作统一到一系列这样的分解核中，该分解核将其纳入我们所提出的分解内核网络（DKNET），用于全面的时空特征提取。与最先进的方法相比，所提出的DKNET经过实验验证以在2倍，3倍和4倍LFSR尺度中达到大量改进。为了进一步完善DKNet，在生产更多视觉上令人愉悦的LFSR结果，我们提出了一个LFVGG丢失来引导纹理增强的DKNet（TE-DKNet）来产生丰富的真实纹理，并显着提高LF图像的视觉质量。我们还通过利用LF材料识别来旨在客观地评估LFVGG损失所带来的感知增强的间接评估度量。

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. The performance of existing methods is still limited, as they produce either blurry results on plain textured areas or distortions around depth discontinuous boundaries. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation, while the other module warps another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations adaptively via the learned attention maps, leading to the final high-resolution LF image with satisfactory results on both plain textured areas and depth discontinuous boundaries. Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy. Extensive experiments on both real and simulated hybrid data demonstrate the significant superiority of our approach over state-of-the-art ones. To the best of our knowledge, this is the first end-to-end deep learning method for LF reconstruction from a real hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission.

光场（LF）摄像机记录了光线的强度和方向，并将3D场景编码为4D LF图像。最近，为各种LF图像处理任务提出了许多卷积神经网络（CNN）。但是，CNN有效地处理LF图像是一项挑战，因为空间和角度信息与不同的差异高度缠绕。在本文中，我们提出了一种通用机制，以将这些耦合信息解开以进行LF图像处理。具体而言，我们首先设计了一类特定领域的卷积，以将LFS与不同的维度解开，然后通过设计特定于任务的模块来利用这些分离的功能。我们的解开机制可以在事先之前很好地纳入LF结构，并有效处理4D LF数据。基于提出的机制，我们开发了三个网络（即distgssr，distgasr和Distgdisp），用于空间超分辨率，角度超分辨率和差异估计。实验结果表明，我们的网络在所有这三个任务上都实现了最先进的性能，这表明了我们解散机制的有效性，效率和一般性。项目页面：https：//yingqianwang.github.io/distglf/。

Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM) and a channel-spatial attention module (CSAM), to model the holistic interdependencies among layers, channels, and positions. Specifically, the proposed LAM adaptively emphasizes hierarchical features by considering correlations among layers. Meanwhile, CSAM learns the confidence at all the positions of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-ofthe-art single image super-resolution approaches.

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.

近年来，在光场（LF）图像超分辨率（SR）中，深度神经网络（DNN）的巨大进展。但是，现有的基于DNN的LF图像SR方法是在单个固定降解（例如，双学的下采样）上开发的，因此不能应用于具有不同降解的超级溶解实际LF图像。在本文中，我们提出了第一种处理具有多个降解的LF图像SR的方法。在我们的方法中，开发了一个实用的LF降解模型，以近似于真实LF图像的降解过程。然后，降解自适应网络（LF-DANET）旨在将降解之前纳入SR过程。通过对具有多种合成降解的LF图像进行训练，我们的方法可以学会适应不同的降解，同时结合了空间和角度信息。对合成降解和现实世界LFS的广泛实验证明了我们方法的有效性。与现有的最新单一和LF图像SR方法相比，我们的方法在广泛的降解范围内实现了出色的SR性能，并且可以更好地推广到真实的LF图像。代码和模型可在https://github.com/yingqianwang/lf-danet上找到。

The Super-Resolution Generative Adversarial Network (SR-GAN) [1] is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGANnetwork architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN [2] to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge 1 [3]. The code is available at https://github.com/xinntao/ESRGAN.

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack highfrequency textures and do not look natural despite yielding high PSNR values.We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.

捕获场景的空间和角度信息的光场（LF）成像无疑是有利于许多应用。尽管已经提出了用于LF采集的各种技术，但是在角度和空间上实现的既仍然是技术挑战。本文，提出了一种基于学习的方法，其应用于3D末面图像（EPI）以重建高分辨率LF。通过2级超分辨率框架，所提出的方法有效地解决了各种LF超分辨率（SR）问题，即空间SR，Angular SR和角空间SR。虽然第一阶段向Up-Sample EPI体积提供灵活的选择，但是由新型EPI体积的细化网络（EVRN）组成的第二阶段，基本上提高了高分辨率EPI体积的质量。从7个发布的数据集的90个挑战合成和实际灯田场景的广泛评估表明，所提出的方法优于空间和角度超分辨率问题的大型延伸的最先进的方法，即平均值峰值信号到噪声比为2.0 dB，1.4 dB和3.14 dB的空间SR $ \ Times 2 $，Spatial SR $ \ Times 4 $和Angular SR。重建的4D光场展示了所有透视图像的平衡性能分布，与先前的作品相比，卓越的视觉质量。

成功地应用生成的对抗性网络（GaN）以研究感知单个图像超级度（SISR）。然而，GaN经常倾向于产生具有高频率细节的图像与真实的细节不一致。灵感来自传统细节增强算法，我们提出了一种新的先前知识，先前的细节，帮助GaN减轻这个问题并恢复更现实的细节。所提出的方法名为DSRAN，包括良好设计的详细提取算法，用于捕获图像中最重要的高频信息。然后，两种鉴别器分别用于在图像域和细节域修复上进行监督。 DSRGAN通过细节增强方式将恢复的细节合并到最终输出中。 DSRGAN的特殊设计从基于模型的常规算法和数据驱动的深度学习网络中获得了优势。实验结果表明，DSRGAN在感知度量上表现出最先进的SISR方法，并同时达到保真度量的可比结果。在DSRGAN之后，将其他传统的图像处理算法结合到深度学习网络中，以形成基于模型的深SISR。

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}}.

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image superresolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4× upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.

图像超分辨率（SR）是重要的图像处理方法之一，可改善计算机视野领域的图像分辨率。在过去的二十年中，在超级分辨率领域取得了重大进展，尤其是通过使用深度学习方法。这项调查是为了在深度学习的角度进行详细的调查，对单像超分辨率的最新进展进行详细的调查，同时还将告知图像超分辨率的初始经典方法。该调查将图像SR方法分类为四个类别，即经典方法，基于学习的方法，无监督学习的方法和特定领域的SR方法。我们还介绍了SR的问题，以提供有关图像质量指标，可用参考数据集和SR挑战的直觉。使用参考数据集评估基于深度学习的方法。一些审查的最先进的图像SR方法包括增强的深SR网络（EDSR），周期循环gan（Cincgan），多尺度残留网络（MSRN），Meta残留密度网络（META-RDN） ，反复反射网络（RBPN），二阶注意网络（SAN），SR反馈网络（SRFBN）和基于小波的残留注意网络（WRAN）。最后，这项调查以研究人员将解决SR的未来方向和趋势和开放问题的未来方向和趋势。

最近，基于深度学习的超分辨率方法取得了良好的性能，但主要关注通过喂养许多样品来训练单个广义的深网络。但是直观地，每个图像都具有其表示，并且预计将获得自适应模型。对于此问题，我们通过利用图像或特征的全局上下文信息来提出一种新颖的图像特异性卷积核调制（IKM），以产生适当地调制卷积核的注意重量，这越优于Vanilla卷积和几个现有的注意机制在没有任何其他参数的情况下嵌入最先进的架构。特别是，为了优化我们在迷你批量培训中的IKM，我们引入了一种特定于图像的优化（ISO）算法，比传统的迷你批量SGD优化更有效。此外，我们调查IKM对最先进的架构的影响，并利用一个带有U风格的残差学习和沙漏密集的块学习的新骨干，术语U-HOLGLASS密集网络（U-HDN），这是一个理论上和实验，最大限度地提高IKM的效力。单图像超分辨率的广泛实验表明，该方法实现了优异的现有方法性能。代码可在github.com/yuanfeihuang/ikm获得。

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistic priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss future directions, including network designs, metrics, benchmark datasets, applications,etc. We also provide an open-source repository for all the discussed methods, which is available at https://github.com/TaoWangzj/Awesome-Face-Restoration.

随着深度学习（DL）的出现，超分辨率（SR）也已成为一个蓬勃发展的研究领域。然而，尽管结果有希望，但该领域仍然面临需要进一步研究的挑战，例如，允许灵活地采样，更有效的损失功能和更好的评估指标。我们根据最近的进步来回顾SR的域，并检查最新模型，例如扩散（DDPM）和基于变压器的SR模型。我们对SR中使用的当代策略进行了批判性讨论，并确定了有前途但未开发的研究方向。我们通过纳入该领域的最新发展，例如不确定性驱动的损失，小波网络，神经体系结构搜索，新颖的归一化方法和最新评估技术来补充先前的调查。我们还为整章中的模型和方法提供了几种可视化，以促进对该领域趋势的全球理解。最终，这篇综述旨在帮助研究人员推动DL应用于SR的界限。

面部超分辨率（FSR），也称为面部幻觉，其旨在增强低分辨率（LR）面部图像以产生高分辨率（HR）面部图像的分辨率，是特定于域的图像超分辨率问题。最近，FSR获得了相当大的关注，并目睹了深度学习技术的发展炫目。迄今为止，有很少有基于深入学习的FSR的研究摘要。在本次调查中，我们以系统的方式对基于深度学习的FSR方法进行了全面审查。首先，我们总结了FSR的问题制定，并引入了流行的评估度量和损失功能。其次，我们详细说明了FSR中使用的面部特征和流行数据集。第三，我们根据面部特征的利用大致分类了现有方法。在每个类别中，我们从设计原则的一般描述开始，然后概述代表方法，然后讨论其中的利弊。第四，我们评估了一些最先进的方法的表现。第五，联合FSR和其他任务以及与FSR相关的申请大致介绍。最后，我们设想了这一领域进一步的技术进步的前景。在\ URL {https://github.com/junjun-jiang/face-hallucination-benchmark}上有一个策划的文件和资源的策划文件和资源清单

Image super-resolution (SR) serves as a fundamental tool for the processing and transmission of multimedia data. Recently, Transformer-based models have achieved competitive performances in image SR. They divide images into fixed-size patches and apply self-attention on these patches to model long-range dependencies among pixels. However, this architecture design is originated for high-level vision tasks, which lacks design guideline from SR knowledge. In this paper, we aim to design a new attention block whose insights are from the interpretation of Local Attribution Map (LAM) for SR networks. Specifically, LAM presents a hierarchical importance map where the most important pixels are located in a fine area of a patch and some less important pixels are spread in a coarse area of the whole image. To access pixels in the coarse area, instead of using a very large patch size, we propose a lightweight Global Pixel Access (GPA) module that applies cross-attention with the most similar patch in an image. In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a $3\times3$ convolution is applied to process the finest details. In addition, a Cascaded Patch Division (CPD) strategy is proposed to enhance perceptual quality of recovered images. Extensive experiments suggest that our method outperforms state-of-the-art lightweight SR methods by a large margin. Code is available at https://github.com/passerer/HPINet.

移动设备上的低光成像通常是由于不足的孔径穿过相对较小的孔径而挑战，导致信噪比较低。以前的大多数关于低光图像处理的作品仅关注单个任务，例如照明调整，颜色增强或删除噪声；或在密切依赖于从特定的摄像机模型中收集的长时间曝光图像对的关节照明调整和降解任务上，因此，这些方法在需要摄像机特定的关节增强和恢复的现实环境中不太实用且可推广。为了解决这个问题，在本文中，我们提出了一个低光图像处理框架，该框架可以执行关节照明调整，增强色彩和降解性。考虑到模型特异性数据收集的难度和捕获图像的超高定义，我们设计了两个分支：系数估计分支以及关节增强和denoising分支。系数估计分支在低分辨率空间中起作用，并预测通过双边学习增强的系数，而关节增强和去核分支在全分辨率空间中工作，并逐步执行关节增强和脱氧。与现有方法相反，我们的框架在适应另一个摄像机模型时不需要回忆大量数据，这大大减少了微调我们用于实际使用方法所需的努力。通过广泛的实验，与当前的最新方法相比，我们在现实世界中的低光成像应用中证明了它的巨大潜力。

深度映射记录场景中的视点和对象之间的距离，这在许多真实应用程序中起着关键作用。然而，消费者级RGB-D相机捕获的深度图遭受了低空间分辨率。引导深度地图超分辨率（DSR）是解决此问题的流行方法，该方法试图从输入的低分辨率（LR）深度及其耦合的HR RGB图像中恢复高分辨率（HR）深度映射和作为指引。引导DSR最具挑战性的问题是如何正确选择一致的结构并传播它们，并正确处理不一致的结构。在本文中，我们提出了一种用于引导DSR的新型关注的分层多模态融合（AHMF）网络。具体地，为了有效地提取和组合来自LR深度和HR引导的相关信息，我们提出了一种基于多模态注意力的融合（MMAF）策略，包括分层卷积层，包括特征增强块，以选择有价值的功能和特征重新校准块来统一不同外观特征的方式的相似性度量。此外，我们提出了一个双向分层特征协作（BHFC）模块，以完全利用多尺度特征之间的低级空间信息和高级结构信息。实验结果表明，在重建精度，运行速度和记忆效率方面，我们的方法优于最先进的方法。