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.

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。

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。

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.

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.

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.

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。

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

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.

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. Therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. The newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.

基于流量的生成超分辨率（SR）模型学会生产一组可行的SR解决方案，称为SR空间。 SR溶液的多样性随着潜在变量的温度（$ \ tau $）的增加而增加，这引入了样品溶液之间纹理的随机变化，从而导致视觉伪像和低忠诚度。在本文中，我们提出了一种简单但有效的图像结合/融合方法，以获得消除随机伪像的单个SR图像，并改善忠诚度，而不会显着损害感知质量。我们通过从流量模型跨越的SR空间中的一系列可行的光真实解决方案中受益，从而实现这一目标。我们提出了不同的图像结合和融合策略，这些策略提供了多种途径，可以根据手头任务的保真度与感知质量要求，以可控的方式将SR Slace样本解决方案移至感知延伸平面中更为理想的目的地。实验结果表明，与流量模型和经过对抗训练的模型所产生的样本SR图像相比，我们的图像结合/融合策略在定量指标和视觉质量方面实现了更有希望的感知依赖权衡。

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。

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.

在本文中，我们发现两个因素抑制POMS从实现高感感性质量：1）方向优化（COO）问题和2）模型的低频趋势。首先，POMS倾向于生成SR图像，其位置空间中的位置最接近所有潜在的高分辨率（HR）图像的分配中心，导致这种POMS失去高频细节。其次，图像的90美元\％$区域由低频信号组成;相比之下，人类感知依赖于图像的高频细节。然而，POMS应用相同的计算来处理不同频率区域，使POM倾向于恢复低频区域。基于这两个因素，我们提出了一种细节，通过组合高频增强模块和空间对比学习模块来降低COO问题的影响和低频趋势来提高对比损失（DECHROSTS）。实验结果表明，在若干常规SR模型上施加DROCKS时的效率和有效性。例如，在EDSR中，与基于GAN的方法相比，我们所提出的方法与视觉质量微妙降级的基于GAN的方法实现了3.60美元。此外，我们的最终结果表明，与最先进的方法相比，配备了我们的DECHROSS的SR网络更具现实和视觉上令人愉悦的纹理。 ％拟议方法的源代码包含在补充材料中，并将在将来公开。

基于参考的图像超分辨率（REFSR）旨在利用辅助参考（REF）图像为超溶解的低分辨率（LR）图像。最近，RefSR引起了极大的关注，因为它提供了超越单图SR的替代方法。但是，解决REFSR问题有两个关键的挑战：（i）当它们显着不同时，很难匹配LR和Ref图像之间的对应关系； （ii）如何将相关纹理从参考图像转移以补偿LR图像的细节非常具有挑战性。为了解决RefSR的这些问题，本文提出了一个可变形的注意变压器，即DATSR，具有多个尺度，每个尺度由纹理特征编码器（TFE）模块组成，基于参考的可变形注意（RDA）模块和残差功能聚合（RFA）模块。具体而言，TFE首先提取图像转换（例如，亮度）不敏感的LR和REF图像，RDA可以利用多个相关纹理来补偿更多的LR功能信息，而RFA最终汇总了LR功能和相关纹理，以获得更愉快的宜人的质地结果。广泛的实验表明，我们的DATSR在定量和质量上实现了基准数据集上的最新性能。

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.

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.

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。

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].

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.