近年来，使用基于深入学习的架构的状态，在图像超分辨率的任务中有几个进步。先前发布的许多基于超分辨率的技术，需要高端和顶部的图形处理单元（GPU）来执行图像超分辨率。随着深度学习方法的进步越来越大，神经网络已经变得越来越多地计算饥饿。我们返回了一步，并专注于创建实时有效的解决方案。我们提出了一种在其内存足迹方面更快更小的架构。所提出的架构使用深度明智的可分离卷积来提取特征，并且它与其他超分辨率的GAN（生成对抗网络）进行接受，同时保持实时推断和低存储器占用。即使在带宽条件不佳，实时超分辨率也能够流式传输高分辨率介质内容。在维持准确性和延迟之间的有效权衡之间，我们能够生产可比较的性能模型，该性能模型是超分辨率GAN的大小的一个 - 八（1/8），并且计算的速度比超分辨率的GAN快74倍。

Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like PSNR and SSIM, our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

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。

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.

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.

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。

在本文中，我们介绍了一种快速运动脱棕色条件的生成对抗网络（FMD-CGAN），其有助于单个图像的盲运动去纹理。 FMD-CGAN在去修改图像后提供令人印象深刻的结构相似性和视觉外观。与其他深度神经网络架构一样，GAN也遭受大型模型大小（参数）和计算。在诸如移动设备和机器人等资源约束设备上部署模型并不容易。借助MobileNet基于MobileNet的架构，包括深度可分离卷积，我们降低了模型大小和推理时间，而不会丢失图像的质量。更具体地说，我们将模型大小与最近的竞争对手相比将3-60倍。由此产生的压缩去掩盖CGAN比其最接近的竞争对手更快，甚至定性和定量结果优于各种最近提出的最先进的盲运动去误紧模型。我们还可以使用我们的模型进行实时映像解擦干任务。标准数据集的当前实验显示了该方法的有效性。

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。

现实的高光谱图像（HSI）超分辨率（SR）技术旨在从其低分辨率（LR）对应物中产生具有更高光谱和空间忠诚的高分辨率（HR）HSI。生成的对抗网络（GAN）已被证明是图像超分辨率的有效深入学习框架。然而，现有GaN的模型的优化过程经常存在模式崩溃问题，导致光谱间不变重建容量有限。这可能导致所生成的HSI上的光谱空间失真，尤其是具有大的升级因子。为了缓解模式崩溃的问题，这项工作提出了一种与潜在编码器（Le-GaN）耦合的新型GaN模型，其可以将产生的光谱空间特征从图像空间映射到潜在空间并产生耦合组件正规化生成的样本。基本上，我们将HSI视为嵌入在潜在空间中的高维歧管。因此，GaN模型的优化被转换为学习潜在空间中的高分辨率HSI样本的分布的问题，使得产生的超分辨率HSI的分布更接近其原始高分辨率对应物的那些。我们对超级分辨率的模型性能进行了实验评估及其在缓解模式崩溃中的能力。基于具有不同传感器（即Aviris和UHD-185）的两种实际HSI数据集进行了测试和验证，用于各种升高因素并增加噪声水平，并与最先进的超分辨率模型相比（即Hyconet，LTTR，Bagan，SR-GaN，Wgan）。

FREDSR is a GAN variant that aims to outperform traditional GAN models in specific tasks such as Single Image Super Resolution with extreme parameter efficiency at the cost of per-dataset generalizeability. FREDSR integrates fast Fourier transformation, residual prediction, diffusive discriminators, etc to achieve strong performance in comparisons to other models on the UHDSR4K dataset for Single Image 3x Super Resolution from 360p and 720p with only 37000 parameters. The model follows the characteristics of the given dataset, resulting in lower generalizeability but higher performance on tasks such as real time up-scaling.

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.

In medical image analysis, low-resolution images negatively affect the performance of medical image interpretation and may cause misdiagnosis. Single image super-resolution (SISR) methods can improve the resolution and quality of medical images. Currently, Generative Adversarial Networks (GAN) based super-resolution models have shown very good performance. Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is one of the practical GAN-based models which is widely used in the field of general image super-resolution. One of the challenges in medical image super-resolution is that, unlike natural images, medical images do not have high spatial resolution. To solve this problem, we can use transfer learning technique and fine-tune the model that has been trained on external datasets (often natural datasets). In our proposed approach, the pre-trained generator and discriminator networks of the Real-ESRGAN model are fine-tuned using medical image datasets. In this paper, we worked on chest X-ray and retinal images and used the STARE dataset of retinal images and Tuberculosis Chest X-rays (Shenzhen) dataset for fine-tuning. The proposed model produces more accurate and natural textures, and its outputs have better detail and resolution compared to the original Real-ESRGAN outputs.

随着深度学习（DL）的出现，超分辨率（SR）也已成为一个蓬勃发展的研究领域。然而，尽管结果有希望，但该领域仍然面临需要进一步研究的挑战，例如，允许灵活地采样，更有效的损失功能和更好的评估指标。我们根据最近的进步来回顾SR的域，并检查最新模型，例如扩散（DDPM）和基于变压器的SR模型。我们对SR中使用的当代策略进行了批判性讨论，并确定了有前途但未开发的研究方向。我们通过纳入该领域的最新发展，例如不确定性驱动的损失，小波网络，神经体系结构搜索，新颖的归一化方法和最新评估技术来补充先前的调查。我们还为整章中的模型和方法提供了几种可视化，以促进对该领域趋势的全球理解。最终，这篇综述旨在帮助研究人员推动DL应用于SR的界限。

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.

单个图像超分辨率（SISR）是一个非常活跃的研究领域。本文通过使用带有双鉴别器的GaN的方法来解决SISR，并将其与注意机制合并。实验结果表明，与其他传统方法相比，GDCA可以产生更尖锐和高令人愉悦的图像。

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.

基于深度学习的单图像超分辨率（SISR）方法引起了人们的关注，并在现代高级GPU上取得了巨大的成功。但是，大多数最先进的方法都需要大量参数，记忆和计算资源，这些参数通常会显示在当前移动设备CPU/NPU上时显示出较低的推理时间。在本文中，我们提出了一个简单的普通卷积网络，该网络具有快速最近的卷积模块（NCNET），该模块对NPU友好，可以实时执行可靠的超级分辨率。提出的最近的卷积具有与最近的UP采样相同的性能，但更快，更适合Android NNAPI。我们的模型可以很容易地在具有8位量化的移动设备上部署，并且与所有主要的移动AI加速器完全兼容。此外，我们对移动设备上的不同张量操作进行了全面的实验，以说明网络体系结构的效率。我们的NCNET在DIV2K 3X数据集上进行了训练和验证，并且与其他有效的SR方法的比较表明，NCNET可以实现高保真SR结果，同时使用更少的推理时间。我们的代码和预估计的模型可在\ url {https://github.com/algolzw/ncnet}上公开获得。

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。

具有高分辨率的视网膜光学相干断层扫描术（八八）对于视网膜脉管系统的定量和分析很重要。然而，八颗图像的分辨率与相同采样频率的视野成反比，这不利于临床医生分析较大的血管区域。在本文中，我们提出了一个新型的基于稀疏的域适应超分辨率网络（SASR），以重建现实的6x6 mm2/低分辨率/低分辨率（LR）八八粒图像，以重建高分辨率（HR）表示。更具体地说，我们首先对3x3 mm2/高分辨率（HR）图像进行简单降解，以获得合成的LR图像。然后，采用一种有效的注册方法在6x6 mm2图像中以其相应的3x3 mm2图像区域注册合成LR，以获得裁切的逼真的LR图像。然后，我们提出了一个多级超分辨率模型，用于对合成数据进行全面监督的重建，从而通过生成的对流策略指导现实的LR图像重建现实的LR图像，该策略允许合成和现实的LR图像可以在特征中统一。领域。最后，新型的稀疏边缘感知损失旨在动态优化容器边缘结构。在两个八八集中进行的广泛实验表明，我们的方法的性能优于最先进的超分辨率重建方法。此外，我们还研究了重建结果对视网膜结构分割的性能，这进一步验证了我们方法的有效性。

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。