通过利用大型内核分解和注意机制，卷积神经网络（CNN）可以在许多高级计算机视觉任务中与基于变压器的方法竞争。但是，由于远程建模的优势，具有自我注意力的变压器仍然主导着低级视野，包括超分辨率任务。在本文中，我们提出了一个基于CNN的多尺度注意网络（MAN），该网络由多尺度的大内核注意力（MLKA）和一个封闭式的空间注意单元（GSAU）组成，以提高卷积SR网络的性能。在我们的MLKA中，我们使用多尺度和栅极方案纠正LKA，以在各种粒度水平上获得丰富的注意图，从而共同汇总了全局和局部信息，并避免了潜在的阻塞伪像。在GSAU中，我们集成了栅极机制和空间注意力，以消除不必要的线性层和汇总信息丰富的空间环境。为了确认我们的设计的有效性，我们通过简单地堆叠不同数量的MLKA和GSAU来评估具有多种复杂性的人。实验结果表明，我们的人可以在最先进的绩效和计算之间实现各种权衡。代码可从https://github.com/icandle/man获得。

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) 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.

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.

随着卷积神经网络最近的大规模发展，已经提出了用于边缘设备上实用部署的大量基于CNN的显着图像超分辨率方法。但是，大多数现有方法都集中在一个特定方面：网络或损失设计，这导致难以最大程度地减少模型大小。为了解决这个问题，我们得出结论，设计，架构搜索和损失设计，以获得更有效的SR结构。在本文中，我们提出了一个名为EFDN的边缘增强功能蒸馏网络，以保留在约束资源下的高频信息。详细说明，我们基于现有的重新处理方法构建了一个边缘增强卷积块。同时，我们提出了边缘增强的梯度损失，以校准重新分配的路径训练。实验结果表明，我们的边缘增强策略可以保持边缘并显着提高最终恢复质量。代码可在https://github.com/icandle/efdn上找到。

基于变压器的方法与基于CNN的方法相比，由于其对远程依赖性的模型，因此获得了令人印象深刻的图像恢复性能。但是，像Swinir这样的进步采用了基于窗口的和本地注意力的策略来平衡性能和计算开销，这限制了采用大型接收领域来捕获全球信息并在早期层中建立长期依赖性。为了进一步提高捕获全球信息的效率，在这项工作中，我们建议Swinfir通过更换具有整个图像范围的接收场的快速傅立叶卷积（FFC）组件来扩展Swinir。我们还重新访问其他先进技术，即数据增强，预训练和功能集合，以改善图像重建的效果。并且我们的功能合奏方法使模型的性能得以大大增强，而无需增加训练和测试时间。与现有方法相比，我们将算法应用于多个流行的大规模基准，并实现了最先进的性能。例如，我们的Swinfir在漫画109数据集上达到了32.83 dB的PSNR，该PSNR比最先进的Swinir方法高0.8 dB。

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from lowquality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced by up to 67%.

卷积神经网络在过去十年中允许在单个图像超分辨率（SISR）中的显着进展。在SISR最近的进展中，关注机制对于高性能SR模型至关重要。但是，注意机制仍然不清楚为什么它在SISR中的工作原理。在这项工作中，我们试图量化和可视化SISR中的注意力机制，并表明并非所有关注模块都同样有益。然后，我们提出了关注网络（A $ ^ 2 $ n）的注意力，以获得更高效和准确的SISR。具体来说，$ ^ 2 $ n包括非关注分支和耦合注意力分支。提出了一种动态注意力模块，为这两个分支产生权重，以动态地抑制不需要的注意力调整，其中权重根据输入特征自适应地改变。这允许注意模块专门从事惩罚的有益实例，从而大大提高了注意力网络的能力，即几个参数开销。实验结果表明，我们的最终模型A $ ^ 2 $ n可以实现与类似尺寸的最先进网络相比的卓越的权衡性能。代码可以在https://github.com/haoyuc/a2n获得。

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and obtained remarkable performance. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper architecture design, neglecting to explore the feature correlations of intermediate layers, hence hindering the representational power of CNNs. To address this issue, in this paper, we propose a second-order attention network (SAN) for more powerful feature expression and feature correlation learning. Specifically, a novel trainable second-order channel attention (SOCA) module is developed to adaptively rescale the channel-wise features by using second-order feature statistics for more discriminative representations. Furthermore, we present a non-locally enhanced residual group (NLRG) structure, which not only incorporates non-local operations to capture long-distance spatial contextual information, but also contains repeated local-source residual attention groups (LSRAG) to learn increasingly abstract feature representations. Experimental results demonstrate the superiority of our SAN network over state-of-the-art SISR methods in terms of both quantitative metrics and visual quality.

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.

Recently, great progress has been made in single-image super-resolution (SISR) based on deep learning technology. However, the existing methods usually require a large computational cost. Meanwhile, the activation function will cause some features of the intermediate layer to be lost. Therefore, it is a challenge to make the model lightweight while reducing the impact of intermediate feature loss on the reconstruction quality. In this paper, we propose a Feature Interaction Weighted Hybrid Network (FIWHN) to alleviate the above problem. Specifically, FIWHN consists of a series of novel Wide-residual Distillation Interaction Blocks (WDIB) as the backbone, where every third WDIBs form a Feature shuffle Weighted Group (FSWG) by mutual information mixing and fusion. In addition, to mitigate the adverse effects of intermediate feature loss on the reconstruction results, we introduced a well-designed Wide Convolutional Residual Weighting (WCRW) and Wide Identical Residual Weighting (WIRW) units in WDIB, and effectively cross-fused features of different finenesses through a Wide-residual Distillation Connection (WRDC) framework and a Self-Calibrating Fusion (SCF) unit. Finally, to complement the global features lacking in the CNN model, we introduced the Transformer into our model and explored a new way of combining the CNN and Transformer. Extensive quantitative and qualitative experiments on low-level and high-level tasks show that our proposed FIWHN can achieve a good balance between performance and efficiency, and is more conducive to downstream tasks to solve problems in low-pixel scenarios.

压缩在通过限制系统（例如流媒体服务，虚拟现实或视频游戏）等系统的有效传输和存储图像和视频中起着重要作用。但是，不可避免地会导致伪影和原始信息的丢失，这可能会严重降低视觉质量。由于这些原因，压缩图像的质量增强已成为流行的研究主题。尽管大多数最先进的图像恢复方法基于卷积神经网络，但基于Swinir等其他基于变压器的方法在这些任务上表现出令人印象深刻的性能。在本文中，我们探索了新型的Swin Transformer V2，以改善图像超分辨率的Swinir，尤其是压缩输入方案。使用这种方法，我们可以解决训练变压器视觉模型中的主要问题，例如训练不稳定性，预训练和微调之间的分辨率差距以及数据饥饿。我们对三个代表性任务进行实验：JPEG压缩伪像去除，图像超分辨率（经典和轻巧）以及压缩的图像超分辨率。实验结果表明，我们的方法SWIN2SR可以改善SWINIR的训练收敛性和性能，并且是“ AIM 2022挑战压缩图像和视频的超分辨率”的前5个解决方案。

随着深度学习的发展，单图像超分辨率（SISR）取得了重大突破。最近，已经提出了基于全局特征交互的SISR网络性能的方法。但是，需要动态地忽略对上下文的响应的神经元的功能。为了解决这个问题，我们提出了一个轻巧的交叉障碍性推理网络（CFIN），这是一个由卷积神经网络（CNN）和变压器组成的混合网络。具体而言，一种新型的交叉磁场导向变压器（CFGT）旨在通过使用调制卷积内核与局部代表性语义信息结合来自适应修改网络权重。此外，提出了基于CNN的跨尺度信息聚合模块（CIAM），以使模型更好地专注于潜在的实用信息并提高变压器阶段的效率。广泛的实验表明，我们提出的CFIN是一种轻巧有效的SISR模型，可以在计算成本和模型性能之间达到良好的平衡。

在本文中，我们呈现了UFFORER，一种用于图像恢复的有效和高效的变换器架构，其中我们使用变压器块构建分层编码器解码器网络。在UFFAR中，有两个核心设计。首先，我们介绍了一个新颖的本地增强型窗口（Lewin）变压器块，其执行基于窗口的自我关注而不是全局自我关注。它显着降低了高分辨率特征映射的计算复杂性，同时捕获本地上下文。其次，我们提出了一种以多尺度空间偏置的形式提出了一种学习的多尺度恢复调制器，以调整UFFORER解码器的多个层中的特征。我们的调制器展示了卓越的能力，用于恢复各种图像恢复任务的详细信息，同时引入边缘额外参数和计算成本。通过这两个设计提供支持，UFFORER享有高能力，可以捕获本地和全局依赖性的图像恢复。为了评估我们的方法，在几种图像恢复任务中进行了广泛的实验，包括图像去噪，运动脱棕，散焦和污染物。没有钟声和口哨，与最先进的算法相比，我们的UFormer实现了卓越的性能或相当的性能。代码和模型可在https://github.com/zhendongwang6/uformer中找到。

将低分辨率（LR）图像恢复到超分辨率（SR）图像具有正确和清晰的细节是挑战。现有的深度学习工作几乎忽略了图像的固有结构信息，这是对SR结果的视觉感知的重要作用。在本文中，我们将分层特征开发网络设计为探测并以多尺度特征融合方式保持结构信息。首先，我们提出了在传统边缘探测器上的交叉卷积，以定位和代表边缘特征。然后，交叉卷积块（CCBS）设计有功能归一化和渠道注意，以考虑特征的固有相关性。最后，我们利用多尺度特征融合组（MFFG）来嵌入交叉卷积块，并在层次的层次上开发不同尺度的结构特征的关系，调用名为Cross-SRN的轻量级结构保护网络。实验结果表明，交叉SRN通过准确且清晰的结构细节实现了对最先进的方法的竞争或卓越的恢复性能。此外，我们设置了一个标准，以选择具有丰富的结构纹理的图像。所提出的跨SRN优于所选择的基准测试的最先进的方法，这表明我们的网络在保存边缘具有显着的优势。

联合超分辨率和反音调映射（联合SR-ITM）旨在增加低分辨率和标准动态范围图像的分辨率和动态范围。重点方法主要是诉诸图像分解技术，使用多支化的网络体系结构。 ，这些方法采用的刚性分解在很大程度上将其力量限制在各种图像上。为了利用其潜在能力，在本文中，我们将分解机制从图像域概括为更广泛的特征域。为此，我们提出了一个轻巧的特征分解聚合网络（FDAN）。特别是，我们设计了一个功能分解块（FDB），可以实现功能细节和对比度的可学习分离。通过级联FDB，我们可以建立一个用于强大的多级特征分解的分层功能分解组。联合SR-ITM，\ ie，SRITM-4K的新基准数据集，该数据集是大规模的，为足够的模型培训和评估提供了多功能方案。两个基准数据集的实验结果表明，我们的FDAN表明我们的FDAN有效，并且胜过了以前的方法sr-itm.ar代码和数据集将公开发布。

Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of tokens are always from a dense area of the image. This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions. Obviously, this strategy could result in restricted receptive fields. To address this issue, we propose Attention Retractable Transformer (ART) for image restoration, which presents both dense and sparse attention modules in the network. The sparse attention module allows tokens from sparse areas to interact and thus provides a wider receptive field. Furthermore, the alternating application of dense and sparse attention modules greatly enhances representation ability of Transformer while providing retractable attention on the input image.We conduct extensive experiments on image super-resolution, denoising, and JPEG compression artifact reduction tasks. Experimental results validate that our proposed ART outperforms state-of-the-art methods on various benchmark datasets both quantitatively and visually. We also provide code and models at the website https://github.com/gladzhang/ART.

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.

已经证明了深度卷积神经网络近年来对SISR有效。一方面，已经广泛使用了残余连接和密集连接，以便于前向信息和向后梯度流动以提高性能。然而，当前方法以次优的方式在大多数网络层中单独使用残留连接和密集连接。另一方面，虽然各种网络和方法旨在改善计算效率，节省参数或利用彼此的多种比例因子的训练数据来提升性能，但它可以在人力资源空间中进行超级分辨率来具有高计算成本或不能在不同尺度因子的模型之间共享参数以节省参数和推理时间。为了解决这些挑战，我们提出了一种使用双路径连接的高效单图像超分辨率网络，其多种规模学习命名为EMSRDPN。通过将双路径的双路径连接引入EMSRDPN，它在大多数网络层中以综合方式使用残差连接和密集连接。双路径连接具有重用残余连接的共同特征和探索密集连接的新功能，以了解SISR的良好代表。要利用多种比例因子的特征相关性，EMSRDPN在不同缩放因子之间共享LR空间中的所有网络单元，以学习共享功能，并且仅使用单独的重建单元进行每个比例因子，这可以利用多种规模因子的培训数据来帮助各个规模因素另外提高性能，同时可以节省参数并支持共享推理，以提高效率的多种规模因素。实验显示EMSRDPN通过SOTA方法实现更好的性能和比较或更好的参数和推理效率。

In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to realworld applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.