引导过滤器是计算机视觉和计算机图形中的基本工具，旨在将结构信息从引导图像传输到目标图像。大多数现有方法构造来自指导本身的滤波器内核，而不考虑指导和目标之间的相互依赖性。然而，由于两种图像中通常存在显着不同的边沿，只需将引导的所有结构信息传送到目标即将导致各种伪像。要应对这个问题，我们提出了一个名为Deep Enterponal引导图像过滤的有效框架，其过滤过程可以完全集成两个图像中包含的互补信息。具体地，我们提出了一种注意力内核学习模块，分别从引导和目标生成双组滤波器内核，然后通过在两个图像之间建模像素方向依赖性来自适应地组合它们。同时，我们提出了一种多尺度引导图像滤波模块，以粗略的方式通过所构造的内核逐渐产生滤波结果。相应地，引入了多尺度融合策略以重用中间导点在粗略的过程中。广泛的实验表明，所提出的框架在广泛的引导图像滤波应用中，诸如引导超分辨率，横向模态恢复，纹理拆除和语义分割的最先进的方法。

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。

引导深度超分辨率（GDSR）是多模态图像处理中的必要主题，其在同一场景的HR RGB图像的帮助下重建与次优条件的低分辨率的高分辨率（HR）深度映射。为了解决解释工作机制的挑战，提取过度转移的跨模型特征和RGB纹理，我们提出了一种新颖的离散余弦变换网络（DCTNet）来缓解三个方面的问题。首先，离散余弦变换（DCT）模块通过使用DCT来解决来自GDSR的图像域的频道明智的优化问题来重建多通道HR深度特征。其次，我们介绍了一个半耦合特征提取模块，使用共享卷积核，以提取公共功能和私有内核，以提取特定的模态特征。第三，我们采用了边缘注意机制，以突出导致导游的轮廓。广泛的定量和定性评估表明了我们的DCTNET的有效性，这优于以前的最先进方法，具有相对较少的参数。代码将公开。

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.

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. The heart of this representation is our proposed Geometric Spatial Aggregator (GSA), which exploits a distance field modulated by arbitrarily upsampled target gridding, through which the geometric information is explicitly introduced into feature aggregation and target generation. Furthermore, bricking with GSA, we present a transformer-style backbone named GeoDSR, which possesses a principled way to construct the functional mapping between local coordinates and the high-resolution output results, empowering our model with the advantage of arbitrary shape transformation ready to help diverse zooming demand. Extensive experimental results on standard depth map benchmarks, e.g., NYU v2, have demonstrated that the proposed framework achieves significant restoration gain in arbitrary scale depth map super-resolution compared with the prior art. Our codes are available at https://github.com/nana01219/GeoDSR.

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.

深度完成旨在预测从深度传感器（例如Lidars）中捕获的极稀疏图的密集像素深度。它在各种应用中起着至关重要的作用，例如自动驾驶，3D重建，增强现实和机器人导航。基于深度学习的解决方案已经证明了这项任务的最新成功。在本文中，我们首次提供了全面的文献综述，可帮助读者更好地掌握研究趋势并清楚地了解当前的进步。我们通过通过对现有方法进行分类的新型分类法提出建议，研究网络体系结构，损失功能，基准数据集和学习策略的设计方面的相关研究。此外，我们在包括室内和室外数据集（包括室内和室外数据集）上进行了三个广泛使用基准测试的模型性能进行定量比较。最后，我们讨论了先前作品的挑战，并为读者提供一些有关未来研究方向的见解。

卷积神经网络（CNN）不仅被广泛普及，而且在包括图像分类，恢复和生成在内的许多应用中都取得了明显的结果。尽管卷积的重量共享特性使它们在各种任务中被广泛采用，但其内容不足的特征也可以视为主要缺点。为了解决这个问题，在本文中，我们提出了一个新型操作，称为Pixel自适应核（PAKA）。 Paka通过从可学习的功能中乘以空间变化的注意力来提供对滤波器重量的方向性。所提出的方法会沿通道和空间方向分别渗入像素自适应的注意图，以使用较少的参数来解决分解模型。我们的方法可以以端到端的方式训练，并且适用于任何基于CNN的模型。此外，我们建议使用PAKA改进的信息聚合模块，称为层次PAKA模块（HPM）。与常规信息聚合模块相比，我们通过在语义细分方面提出最先进的性能来证明HPM的优势。我们通过其他消融研究来验证提出的方法，并可视化PAKA的效果，从而为卷积的权重提供了方向性。我们还通过将其应用于多模式任务，尤其是颜色引导的深度图超分辨率来显示该方法的普遍性。

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.

Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://github.com/taozh2017/FAPNet.

随着移动设备的快速开发，现代使用的手机通常允许用户捕获4K分辨率（即超高定义）图像。然而，对于图像进行示范，在低级视觉中，一项艰巨的任务，现有作品通常是在低分辨率或合成图像上进行的。因此，这些方法对4K分辨率图像的有效性仍然未知。在本文中，我们探索了Moire模式的删除，以进行超高定义图像。为此，我们提出了第一个超高定义的演示数据集（UHDM），其中包含5,000个现实世界4K分辨率图像对，并对当前最新方法进行基准研究。此外，我们提出了一个有效的基线模型ESDNET来解决4K Moire图像，其中我们构建了一个语义对准的比例感知模块来解决Moire模式的尺度变化。广泛的实验表明了我们的方法的有效性，这可以超过最轻巧的优于最先进的方法。代码和数据集可在https://xinyu-andy.github.io/uhdm-page上找到。

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.

神经网络的高计算成本阻止了RGB-D突出物体检测（SOD）的最新成功，从受益现实世界应用。因此，本文介绍了一种新颖的网络，Mobily，它专注于使用移动网络进行深度特征提取的高效RGB-D SOD。然而，移动网络在特征表示中的功能较小比麻烦的网络更强大。为此，我们观察到彩色图像的深度信息可以加强与SOD相关的特征表示，如果正确杠杆。因此，我们提出了一种隐式深度恢复（IDR）技术，以加强用于RGB-D SOD的移动网络的特征表示能力。 IDR仅在训练阶段采用并在测试期间省略，因此它是免费的。此外，我们提出了用于有效的多级特征聚合的紧凑金字塔精制（CPR），以获得具有清晰边界的突出对象。与IDR和CPR合并，Mobilesal在六个挑战RGB-D SOD数据集上具有更快的速度（450fps 320 $ 320的输入尺寸为320美元）和更少的参数（6.5米）。代码在https://mmcheng.net/mobilesal发布。

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

图像平滑是一项基本的低级视觉任务，旨在保留图像的显着结构，同时删除微不足道的细节。图像平滑中已经探索了深度学习，以应对语义结构和琐碎细节的复杂纠缠。但是，当前的方法忽略了平滑方面的两个重要事实：1）受限数量的高质量平滑地面真相监督的幼稚像素级回归可能会导致域的转移，并导致对现实世界图像的概括问题； 2）纹理外观与对象语义密切相关，因此图像平滑需要意识到语义差异以应用自适应平滑强度。为了解决这些问题，我们提出了一个新颖的对比语义引导的图像平滑网络（CSGIS-NET），该网络在促进强大的图像平滑之前结合了对比的先验和语义。通过利用不希望的平滑效应作为负面教师，并结合分段任务以鼓励语义独特性来增强监督信号。为了实现所提出的网络，我们还使用纹理增强和平滑标签（即VOC-Smooth）丰富了原始的VOC数据集，它们首先桥接图像平滑和语义分割。广泛的实验表明，所提出的CSGI-NET大量优于最先进的算法。代码和数据集可在https://github.com/wangjie6866/csgis-net上找到。

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.

Pansharpening是指具有高空间分辨率的全色图像的融合和具有低空间分辨率的多光谱图像，旨在获得高空间分辨率多光谱图像。在本文中，我们提出了一种新的深度神经网络架构，通过考虑以下双型结构，\ emph {ie，double级，双分支和双向，称为三双网络（TDNet）。通过使用TDNet的结构，可以充分利用平面图像的空间细节，并利用逐步注入低空间分辨率多光谱图像，从而产生高空间分辨率输出。特定的网络设计是由传统多分辨率分析（MRA）方法的物理公式的动机。因此，有效的MRA融合模块也集成到TDNet中。此外，我们采用了一些Reset块和一些多尺度卷积内核来加深和扩大网络，以有效增强所提出的TDNet的特征提取和鲁棒性。关于WorldView-3，Quickbird和GaoFen-2传感器获得的减少和全分辨率数据集的广泛实验表明了与最近最近的最先进的泛红花彭化方法相比，所提出的TDNet的优越性。一个消融的研究也证实了所提出的方法的有效性。

Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects:(1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring. The code is available at https://github.com/xinntao/EDVR.

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。

联合超分辨率和反音调映射（SR-ITM）旨在提高具有分辨率和动态范围具有质量缺陷的视频的视觉质量。当使用4K高动态范围（HDR）电视来观看低分辨率标准动态范围（LR SDR）视频时，就会出现此问题。以前依赖于学习本地信息的方法通常在保留颜色合规性和远程结构相似性方面做得很好，从而导致了不自然的色彩过渡和纹理伪像。为了应对这些挑战，我们建议联合SR-ITM的全球先验指导的调制网络（GPGMNET）。特别是，我们设计了一个全球先验提取模块（GPEM），以提取颜色合规性和结构相似性，分别对ITM和SR任务有益。为了进一步利用全球先验并保留空间信息，我们使用一些用于中间特征调制的参数，设计多个全球先验的指导空间调制块（GSMB），其中调制参数由共享的全局先验和空间特征生成来自空间金字塔卷积块（SPCB）的地图。通过这些精心设计的设计，GPGMNET可以通过较低的计算复杂性实现更高的视觉质量。广泛的实验表明，我们提出的GPGMNET优于最新方法。具体而言，我们提出的模型在PSNR中超过了0.64 dB的最新模型，其中69 $ \％$ $ $较少，3.1 $ \ times $ speedup。该代码将很快发布。