最近的研究通过卷积神经网络（CNNS）显着提高了单图像超分辨率（SR）的性能。虽然可以有许多用于给定输入的高分辨率（HR）解决方案，但大多数现有的基于CNN的方法在推理期间不会探索替代解决方案。获得替代SR结果的典型方法是培训具有不同丢失权重的多个SR模型，并利用这些模型的组合。我们通过利用多任务学习，我们提出了一种更有效的方法来培训单个可调SR模型的单一可调SR模型。具体地，我们在训练期间优化具有条件目标的SR模型，其中目标是不同特征级别的多个感知损失的加权之和。权重根据给定条件而变化，并且该组重量被定义为样式控制器。此外，我们提出了一种适用于该训练方案的架构，该架构是配备有空间特征变换层的残留残余密集块。在推理阶段，我们培训的模型可以在样式控制地图上生成局部不同的输出。广泛的实验表明，所提出的SR模型在没有伪影的情况下产生各种所需的重建，并对最先进的SR方法产生相当的定量性能。

Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code and models are available at https://github.com/seungho-snu/SROOE.

Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible to recover textures faithful to semantic classes. In particular, we only need to modulate features of a few intermediate layers in a single network conditioned on semantic segmentation probability maps. This is made possible through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. SFT layers can be trained end-to-end together with the SR network using the same loss function. During testing, it accepts an input image of arbitrary size and generates a high-resolution image with just a single forward pass conditioned on the categorical priors. Our final results show that an SR network equipped with SFT can generate more realistic and visually pleasing textures in comparison to state-of-the-art SRGAN [27] and EnhanceNet [38].

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.

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。

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.

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。

现实世界图像超分辨率（SR）的关键挑战是在低分辨率（LR）图像中恢复具有复杂未知降解（例如，下采样，噪声和压缩）的缺失细节。大多数以前的作品还原图像空间中的此类缺失细节。为了应对自然图像的高度多样性，他们要么依靠难以训练和容易训练和伪影的不稳定的甘体，要么诉诸于通常不可用的高分辨率（HR）图像中的明确参考。在这项工作中，我们提出了匹配SR（FEMASR）的功能，该功能在更紧凑的特征空间中恢复了现实的HR图像。与图像空间方法不同，我们的FEMASR通过将扭曲的LR图像{\ IT特征}与我们预读的HR先验中的无失真性HR对应物匹配来恢复HR图像，并解码匹配的功能以获得现实的HR图像。具体而言，我们的人力资源先验包含一个离散的特征代码簿及其相关的解码器，它们在使用量化的生成对抗网络（VQGAN）的HR图像上预估计。值得注意的是，我们在VQGAN中结合了一种新型的语义正则化，以提高重建图像的质量。对于功能匹配，我们首先提取由LR编码器组成的LR编码器的LR功能，然后遵循简单的最近邻居策略，将其与预读的代码簿匹配。特别是，我们为LR编码器配备了与解码器的残留快捷方式连接，这对于优化功能匹配损耗至关重要，还有助于补充可能的功能匹配错误。实验结果表明，我们的方法比以前的方法产生更现实的HR图像。代码以\ url {https://github.com/chaofengc/femasr}发布。

我们考虑单个图像超分辨率（SISR）问题，其中基于低分辨率（LR）输入产生高分辨率（HR）图像。最近，生成的对抗性网络（GANS）变得幻觉细节。大多数沿着这条线的方法依赖于预定义的单个LR-intle-hr映射，这对于SISR任务来说是足够灵活的。此外，GaN生成的假细节可能经常破坏整个图像的现实主义。我们通过为Rich-Detail SISR提出最好的伙伴GANS（Beby-GaN）来解决这些问题。放松不变的一对一的约束，我们允许估计的贴片在培训期间动态寻求最佳监督，这有利于产生更合理的细节。此外，我们提出了一种区域感知的对抗性学习策略，指导我们的模型专注于自适应地为纹理区域发电细节。广泛的实验证明了我们方法的有效性。还构建了超高分辨率4K数据集以促进未来的超分辨率研究。

在图像超分辨率中，需要像素的精度和感知忠诚度。但是，大多数深度学习方法仅在一个方面才能在一个方面实现高性能，并且由于感知能力的权衡，成功平衡权衡取舍的工作取决于从单独培训的模型和临时后处理的融合。在本文中，我们提出了一个具有低频约束（LFC-SR）的新型超分辨率模型，该模型通过单个模型平衡了客观和感知质量，并产生具有较高PSNR和知觉得分的超级分辨图像。我们进一步介绍了一种基于ADMM的交替优化方法，用于对受约束模型的非平凡学习。实验表明，我们的方法，没有麻烦的后处理程序，实现了最新的性能。该代码可在https://github.com/yuehan717/pdasr上找到。

超级分辨率是一个不良问题，其中基本真理的高分辨率图像仅代表合理解决方案的空间中的一种可能性。然而，主导范式是采用像素 - 明智的损失，例如L_1，其驱动预测模糊的平均值。当与对抗性损失相结合时，这导致了根本相互矛盾的目标，这降低了最终质量。我们通过重新审视L_1丢失来解决此问题，并表明它对应于单层条件流程。灵感来自这一关系，我们探讨了一般流动作为L_1目标的忠诚替代品。我们证明，在与对抗性损失结合时，更深流量的灵活性导致更好的视觉质量和一致性。我们对三个数据集和比例因子进行广泛的用户研究，其中我们的方法被证明了为光逼真的超分辨率优于最先进的方法。代码和培训的型号可在：git.io/adflow

尽管应用于自然图像的大量成功的超分辨率重建（SRR）模型，但它们在遥感图像中的应用往往会产生差的结果。遥感图像通常比自然图像更复杂，并且具有较低分辨率的特殊性，它包含噪音，并且通常描绘了大质感表面。结果，将非专业的SRR模型应用于遥感图像，从而导致人工制品和不良的重建。为了解决这些问题，本文提出了一种受到先前研究工作启发的体系结构，引入了一种新的方法来迫使SRR模型输出现实的遥感图像：而不是依靠功能空间相似性作为感知损失，而是将其视为Pixel-从图像的归一化数字表面模型（NDSM）推断出的级别信息。该策略允许在训练模型期间应用更具信息的更新，该模型从任务（高程图推理）源中源，该模型与遥感密切相关。但是，在生产过程中不需要NDSM辅助信息，因此该模型除了其低分辨率对以外没有任何其他数据，因此该模型还没有任何其他数据。我们在两个远程感知的不同空间分辨率的数据集上评估了我们的模型，这些数据集也包含图像的DSM对：DFC2018数据集和包含卢森堡国家激光雷达飞行的数据集。根据视觉检查，推断的超分辨率图像表现出特别优越的质量。特别是，高分辨率DFC2018数据集的结果是现实的，几乎与地面真相图像没有区别。

单像超分辨率可以在需要可靠的视觉流以监视任务，处理远程操作或研究相关视觉细节的环境中支持机器人任务。在这项工作中，我们为实时超级分辨率提出了一个有效的生成对抗网络模型。我们采用了原始SRGAN的量身定制体系结构和模型量化，以提高CPU和Edge TPU设备上的执行，最多达到200 fps的推断。我们通过将其知识提炼成较小版本的网络，进一步优化我们的模型，并与标准培训方法相比获得显着的改进。我们的实验表明，与较重的最新模型相比，我们的快速和轻量级模型可保持相当令人满意的图像质量。最后，我们对图像传输进行带宽降解的实验，以突出提出的移动机器人应用系统的优势。

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.

尽管基准数据集的成功，但大多数先进的面部超分辨率模型在真实情况下表现不佳，因为真实图像与合成训练对之间的显着域间隙。为了解决这个问题，我们提出了一种用于野外面部超分辨率的新型域 - 自适应降级网络。该降级网络预测流场以及中间低分辨率图像。然后，通过翘曲中间图像来生成降级的对应物。利用捕获运动模糊的偏好，这种模型在保护原始图像和劣化之间保持身份一致性更好地执行。我们进一步提出了超分辨率网络的自我调节块。该块将输入图像作为条件术语，以有效地利用面部结构信息，从而消除了对显式前沿的依赖性，例如，面部地标或边界。我们的模型在Celeba和真实世界的面部数据集上实现了最先进的性能。前者展示了我们所提出的建筑的强大生成能力，而后者展示了现实世界中的良好的身份一致性和感知品质。

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.

We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively. TTSR consists of four closely-related modules optimized for image generation tasks, including a learnable texture extractor by DNN, a relevance embedding module, a hard-attention module for texture transfer, and a softattention module for texture synthesis. Such a design encourages joint feature learning across LR and Ref images, in which deep feature correspondences can be discovered by attention, and thus accurate texture features can be transferred. The proposed texture transformer can be further stacked in a cross-scale way, which enables texture recovery from different levels (e.g., from 1× to 4× magnification). Extensive experiments show that TTSR achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations. The source code can be downloaded at https://github.com/ researchmm/TTSR.

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image superresolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.

对比学习在各种高级任务中取得了显着的成功，但是为低级任务提出了较少的方法。采用VANILLA对比学习技术采用直接为低级视觉任务提出的VANILLA对比度学习技术，因为所获得的全局视觉表现不足以用于需要丰富的纹理和上下文信息的低级任务。在本文中，我们提出了一种用于单图像超分辨率（SISR）的新型对比学习框架。我们从两个视角调查基于对比的学习的SISR：样品施工和特征嵌入。现有方法提出了一些天真的样本施工方法（例如，考虑到作为负样本的低质量输入以及作为正样品的地面真理），并且它们采用了先前的模型（例如，预先训练的VGG模型）来获得该特征嵌入而不是探索任务友好的。为此，我们向SISR提出了一个实用的对比学习框架，涉及在频率空间中产生许多信息丰富的正负样本。我们不是利用其他预先训练的网络，我们设计了一种从鉴别器网络继承的简单但有效的嵌入网络，并且可以用主SR网络迭代优化，使其成为任务最通报。最后，我们对我们的方法进行了广泛的实验评估，与基准方法相比，在目前的最先进的SISR方法中显示出高达0.21 dB的显着增益。