尽管已经通过深度学习技术开发了凝视估计方法，但没有采取诸如以50像素或更少的像素宽度或更少的像素宽度的低分辨率面部图像中准确性能的方法。为了在具有挑战性的低分辨率条件下解决限制，我们提出了高频专注的超级分辨凝视估计网络，即Haze-Net。我们的网络改善了输入图像的分辨率，并通过基于高频注意力块提出的超级分辨率模块增强了眼睛特征和这些边界。此外，我们的凝视估计模块利用眼睛的高频组件以及全球外观图。我们还利用面部的结构位置信息来近似头姿势。实验结果表明，即使在具有28x28像素的低分辨率面部图像中，提出的方法也表现出强大的凝视估计性能。该工作的源代码可在https://github.com/dbseorms16/haze_net/上获得。

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.

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.

盲级超分辨率（SR）旨在从低分辨率（LR）图像中恢复高质量的视觉纹理，通常通过下采样模糊内核和添加剂噪声来降解。由于现实世界中复杂的图像降解的挑战，此任务非常困难。现有的SR方法要么假定预定义的模糊内核或固定噪声，这限制了这些方法在具有挑战性的情况下。在本文中，我们提出了一个用于盲目超级分辨率（DMSR）的降解引导的元修复网络，该网络促进了真实病例的图像恢复。 DMSR由降解提取器和元修复模块组成。萃取器估计LR输入中的降解，并指导元恢复模块以预测恢复参数的恢复参数。 DMSR通过新颖的降解一致性损失和重建损失共同优化。通过这样的优化，DMSR在三个广泛使用的基准上以很大的边距优于SOTA。一项包括16个受试者的用户研究进一步验证了现实世界中的盲目SR任务中DMSR的优势。

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.

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

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.

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.

当前的深层图像超分辨率（SR）方法试图从下采样的图像或假设简单高斯内核和添加噪声中降解来恢复高分辨率图像。但是，这种简单的图像处理技术代表了降低图像分辨率的现实世界过程的粗略近似。在本文中，我们提出了一个更现实的过程，通过引入新的内核对抗学习超分辨率（KASR）框架来处理现实世界图像SR问题，以降低图像分辨率。在提议的框架中，降解内核和噪声是自适应建模的，而不是明确指定的。此外，我们还提出了一个迭代监督过程和高频选择性目标，以进一步提高模型SR重建精度。广泛的实验验证了对现实数据集中提出的框架的有效性。

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.

Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light degradation prediction network to regress the degradation vector to simulate the real-world degradations, upon which the channel splitting vector is generated as the input for an efficient SR model. Then, a learnable octave convolution block is proposed to adaptively decide the channel splitting scale for low- and high-frequency features at each block, reducing computation overhead and memory cost by offering the large scale to low-frequency features and the small scale to the high ones. To further improve the RISR performance, Non-local regularization is employed to supplement the knowledge of patches from LR and HR subspace with free-computation inference. Extensive experiments demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our DCS-RISR not only achieves the best trade-off between computation/parameter and PSNR/SSIM metric, and also effectively handles real-world images with different degradation levels.

单个图像超分辨率（SISR）是一个不良问题，旨在获得从低分辨率（LR）输入的高分辨率（HR）输出，在此期间应该添加额外的高频信息以改善感知质量。现有的SISR工作主要通过最小化平均平方重建误差来在空间域中运行。尽管高峰峰值信噪比（PSNR）结果，但难以确定模型是否正确地添加所需的高频细节。提出了一些基于基于残余的结构，以指导模型暗示高频率特征。然而，由于空间域度量的解释是有限的，如何验证这些人为细节的保真度仍然是一个问题。在本文中，我们提出了频率域视角来的直观管道，解决了这个问题。由现有频域的工作启发，我们将图像转换为离散余弦变换（DCT）块，然后改革它们以获取DCT功能映射，它用作我们模型的输入和目标。设计了专门的管道，我们进一步提出了符合频域任务的性质的频率损失功能。我们的SISR方法在频域中可以明确地学习高频信息，为SR图像提供保真度和良好的感知质量。我们进一步观察到我们的模型可以与其他空间超分辨率模型合并，以提高原始SR输出的质量。

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The lowresolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

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。

盲人恢复通常会遇到各种规模的面孔输入，尤其是在现实世界中。但是，当前的大多数作品都支持特定的规模面，这限制了其在现实情况下的应用能力。在这项工作中，我们提出了一个新颖的尺度感知盲人面部修复框架，名为FaceFormer，该框架将面部特征恢复作为比例感知转换。所提出的面部特征上采样（FFUP）模块基于原始的比例比例动态生成UPSMPLING滤波器，这有助于我们的网络适应任意面部尺度。此外，我们进一步提出了面部特征嵌入（FFE）模块，该模块利用变压器来层次提取面部潜在的多样性和鲁棒性。因此，我们的脸部形式实现了富裕性和稳健性，恢复了面部的面孔，对面部成分具有现实和对称的细节。广泛的实验表明，我们提出的使用合成数据集训练的方法比当前的最新图像更好地推广到天然低质量的图像。

最新的多视图多媒体应用程序在高分辨率（HR）视觉体验与存储或带宽约束之间挣扎。因此，本文提出了一个多视图图像超分辨率（MVISR）任务。它旨在增加从同一场景捕获的多视图图像的分辨率。一种解决方案是将图像或视频超分辨率（SR）方法应用于低分辨率（LR）输入视图结果。但是，这些方法无法处理视图之间的大角度转换，并利用所有多视图图像中的信息。为了解决这些问题，我们提出了MVSRNET，该MVSRNET使用几何信息从所有LR多视图中提取尖锐的细节，以支持LR输入视图的SR。具体而言，MVSRNET中提出的几何感知参考合成模块使用几何信息和所有多视图LR图像来合成像素对齐的HR参考图像。然后，提出的动态高频搜索网络完全利用了SR参考图像中的高频纹理细节。关于几个基准测试的广泛实验表明，我们的方法在最新方法上有了显着改善。

Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.

Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to implement image SR, so a new trend is to involve frequency-domain information in SR tasks. Besides, image SR is typically application-oriented and various computer vision tasks call for image arbitrary magnification. Therefore, in this paper, we study image features in the frequency domain to design a novel scale-arbitrary image SR network. First, we statistically analyze LR-HR image pairs of several datasets under different scale factors and find that the high-frequency spectra of different images under different scale factors suffer from different degrees of degradation, but the valid low-frequency spectra tend to be retained within a certain distribution range. Then, based on this finding, we devise an adaptive scale-aware feature division mechanism using deep reinforcement learning, which can accurately and adaptively divide the frequency spectrum into the low-frequency part to be retained and the high-frequency one to be recovered. Finally, we design a scale-aware feature recovery module to capture and fuse multi-level features for reconstructing the high-frequency spectrum at arbitrary scale factors. Extensive experiments on public datasets show the superiority of our method compared with state-of-the-art methods.

在本文中，我们提出了D2C-SR，这是一个新颖的框架，用于实现现实世界图像超级分辨率的任务。作为一个不适的问题，超分辨率相关任务的关键挑战是给定的低分辨率输入可能会有多个预测。大多数基于经典的深度学习方法都忽略了基本事实，缺乏对基础高频分布的明确建模，从而导致结果模糊。最近，一些基于GAN或学习的超分辨率空间的方法可以生成模拟纹理，但不能保证具有低定量性能的纹理的准确性。重新思考这两者，我们以离散形式了解了基本高频细节的分布，并提出了两阶段的管道：分歧阶段到收敛阶段。在发散阶段，我们提出了一个基于树的结构深网作为差异骨干。提出了发散损失，以鼓励基于树的网络产生的结果，以分解可能的高频表示，这是我们对基本高频分布进行离散建模的方式。在收敛阶段，我们分配空间权重以融合这些不同的预测，以获得更准确的细节，以获取最终输出。我们的方法为推理提供了方便的端到端方式。我们对几个现实世界基准进行评估，包括具有X8缩放系数的新提出的D2CrealSR数据集。我们的实验表明，D2C-SR针对最先进的方法实现了更好的准确性和视觉改进，参数编号明显较少，并且我们的D2C结构也可以作为广义结构应用于其他一些方法以获得改进。我们的代码和数据集可在https://github.com/megvii-research/d2c-sr上找到

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.