X射线微型计算机断层扫描（Micro-CT）已被广泛利用，以在地下多孔岩石中表征孔隙尺度几何形状。使用深度学习的超分辨率（SR）方法的最新进程允许在大型空间尺度上进行数字增强低分辨率（LR）图像，从而创建与高分辨率（HR）地理真理相当的SR图像。这避免了传统的解决方案和视野折衷。出色的问题是使用配对（已注册的）LR和HR数据，这些数据通常需要在此类方法的训练步骤中，但难以获得。在这项工作中，我们严格比较两种不同的最先进的SR深度学习技术，使用两者和未配对数据，具有类似于类似的地面真理数据。第一方法需要配对的图像来训练卷积神经网络（CNN），而第二种方法使用未配对的图像来训练生成的对抗网络（GaN）。使用具有复杂的微孔纹理的微型CT碳酸盐岩样品进行比较两种方法。我们实现了基于图像的各种图像和数值验证和实验验证，以定量评估两种方法的物理精度和敏感性。我们的定量结果表明，未配对GaN方法可以将超分辨率图像重建为精确，如配对的CNN方法，具有可比的训练时间和数据集要求。这将使用未配对的深度学习方法解除微型CT图像增强的新应用;数据处理阶段不再需要图像注册。来自数据存储平台的解耦图像可以更有效地利用用于培训SR数字岩体应用的网络。这为异构多孔介质中的多尺度流模拟各种应用开辟了新的途径。

X射线微型计算机断层摄影成像中存在固有的视野和分辨率折衷，这限制了多尺寸多孔系统的表征，分析和模型开发。在本文中，我们通过开发3D增强的深层超分辨率（EDSR）卷积神经网络来克服这些权衡来通过来自低分辨率数据的大型空间尺度创建增强的高分辨率数据。配对高分辨率（HR，2 $ \ MU $ M）和低分辨率（LR，6 $ \ MU $ M）来自Bentheimer Rock样本的图像数据用于培训网络。来自训练样本的未见LR和HR数据以及具有不同微结构的另一个样本，用于验证具有各种度量的网络：文本分析，分段行为和孔网络模型（PNM）多相流模拟。经过验证的EDSR网络用于为每个长度为6-7厘米的全核样品生成约1000个高分辨率转速子图像（总图像大小为约6000x6000x32000体素）。每个子培养物都具有从PNMS预测的不同的岩石物理特性，它们组合以创建每个样本的3D连续级模型。在一系列分数流动下模拟低毛细管数不混溶的流动，并直接在1：1的基础上与实验压力和3D饱和度进行比较。 EDSR产生的模型比在存在异质性存在下预测实验行为的基础LR模型更准确，特别是在遇到孔隙尺寸的广泛分布的流动状态下。该模型通常在预测到在实验重复性和三个数量级的实验重复性和相对渗透率内的饱和度准确。所示的工作流程是一个完全预测的，无需校准，并且打开了在真正的多尺度异构系统中的图像，模拟和分析流动的可能性。

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。

High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and patient cooperation ability, isotropic 3D HR image acquisition typically requests long scan time and, results in small spatial coverage and low SNR. Recent studies showed that, with deep convolutional neural networks, isotropic HR MR images could be recovered from low-resolution (LR) input via single image super-resolution (SISR) algorithms. However, most existing SISR methods tend to approach a scale-specific projection between LR and HR images, thus these methods can only deal with a fixed up-sampling rate. For achieving different up-sampling rates, multiple SR networks have to be built up respectively, which is very time-consuming and resource-intensive. In this paper, we propose ArSSR, an Arbitrary Scale Super-Resolution approach for recovering 3D HR MR images. In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images. Then the SR task is converted to represent the implicit voxel function via deep neural networks from a set of paired HR-LR training examples. The ArSSR model consists of an encoder network and a decoder network. Specifically, the convolutional encoder network is to extract feature maps from the LR input images and the fully-connected decoder network is to approximate the implicit voxel function. Due to the continuity of the learned function, a single ArSSR model can achieve arbitrary up-sampling rate reconstruction of HR images from any input LR image after training. Experimental results on three datasets show that the ArSSR model can achieve state-of-the-art SR performance for 3D HR MR image reconstruction while using a single trained model to achieve arbitrary up-sampling scales.

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）。

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。

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.

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.

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.

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。

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.

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

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.

Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality.

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.

单图超分辨率（SISR）的最新方法在从低分辨率（LR）图像产生高分辨率（HR）图像方面表现出了出色的性能。但是，这些方法中的大多数使用合成生成的LR图像显示出它们的优势，并且它们对现实世界图像的推广性通常并不令人满意。在本文中，我们注意针对可靠的超级分辨率（SR）开发的两种著名策略，即基于参考的SR（REFSR）和零摄影SR（ZSSR），并提出了一种综合解决方案，称为参考 - 基于零击SR（RZSR）。遵循ZSSR的原理，我们使用仅从输入图像本身提取的训练样本在测试时间训练特定于图像的SR网络。为了推进ZSSR，我们获得具有丰富纹理和高频细节的参考图像贴片，这些贴片也仅使用跨尺度匹配从输入图像中提取。为此，我们使用深度信息构建了一个内部参考数据集并从数据集中检索参考图像补丁。使用LR贴片及其相应的HR参考贴片，我们训练由非本地注意模块体现的REFSR网络。实验结果证明了与以前的ZSSR方法相比，与其他完全监督的SISR方法相比，所提出的RZSR的优越性与前所未有的图像相比。

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。

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。

自从Dong等人的第一个成功以来，基于深度学习的方法已在单像超分辨率领域中占主导地位。这取代了使用深神经网络的传统基于稀疏编码方法的所有手工图像处理步骤。与明确创建高/低分辨率词典的基于稀疏编码的方法相反，基于深度学习的方法中的词典被隐式地作为多种卷积的非线性组合被隐式获取。基于深度学习方法的缺点是，它们的性能因与训练数据集（室外图像）不同的图像而降低。我们提出了一个带有深层字典（SRDD）的端到端超分辨率网络，在该网络中，高分辨率词典在不牺牲深度学习优势的情况下明确学习。广泛的实验表明，高分辨率词典的显式学习使网络在维持内域测试图像的性能的同时更加强大。

高动态范围（HDR）成像是一种允许广泛的动态曝光范围的技术，这在图像处理，计算机图形和计算机视觉中很重要。近年来，使用深度学习（DL），HDR成像有重大进展。本研究对深层HDR成像方法的最新发展进行了综合和富有洞察力的调查和分析。在分层和结构上，将现有的深层HDR成像方法基于（1）输入曝光的数量/域，（2）学习任务数，（3）新传感器数据，（4）新的学习策略，（5）应用程序。重要的是，我们对关于其潜在和挑战的每个类别提供建设性的讨论。此外，我们审查了深度HDR成像的一些关键方面，例如数据集和评估指标。最后，我们突出了一些打开的问题，并指出了未来的研究方向。