With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations. Up to now, however, such approaches have been exclusively limited to single image super-resolution. The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts. In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way. The proposed architecture naturally leverages information from previous frames due to its recurrent architecture, i.e. the input to the generator is composed of the low-resolution image and, additionally, the warped output of the network at the previous step. Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences. The experimental validation of our algorithm shows the effectiveness of our approach which obtains images with high perceptual quality and improved temporal consistency.
The Unanticipated Asymmetry Between Perceptual Optimization and Assessment
Institute of Microelectronics of the Chinese Academy of Sciences（中国科学院微电子研究所）；Beihang University（北航）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Northwestern University（西北大学）；Universidad de Granada（格拉纳达大学）
视频超分辨率（VSR）已成为视频处理中的关键问题之一。在深度学习文献中，最近的工作已经显示出使用基于对抗性和感知损失来提高各种图像复原任务的性能的好处;然而，这些还没有应用于视频超分辨率。在这项工作中，我们提出了一个生成对抗网络（GAN）为基础的制定VSR。我们引入了一个新的生成器网络优化的VSR问题，命名为VSRResNet，以及一个新的神经网络架构，以正确指导VSRResNet在GAN训练。我们进一步增强了我们的VSR GAN制定与两个正则化，在特征空间和像素空间的距离损失，以获得我们最终的VSRResistance GAN模型。我们表明，用均方误差损失预训练我们的生成器仅在数量上超过当前最先进的VSR模型。最后，我们采用PercepDist度量（Zhang et al.，2018）来比较最先进的VSR模型。我们表明，与常用的PSNR/SSIM指标相比，该指标更准确地评估从神经网络获得的SR解决方案的感知质量。最后，我们表明，我们提出的模型，VSRRescultGAN模型，优于目前最先进的SR模型，无论是定量和定性。
This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images. The previous approaches use several loss functions which is hard to interpret and has the implicit relationships to improve the perceptual score. We show how to exploit the machine learning based model which is directly trained to provide the perceptual score on generated images. It is believed that these models can be used to optimizes the super-resolution network which is easier to interpret. We further analyze the characteristic of the existing loss and our proposed explicit…
Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However, due to the complex degradation in natural scenes, recovering high-resolution texts from the low-resolution inputs is ambiguous and challenging. Existing methods mainly leverage deep neural networks trained with pixel-wise losses designed for natural image reconstruction, which ignore the unique character characteristics of texts. A few works proposed content-based losses. However, they only focus on text recognizers'…
Signal Processing Lab (LTS5) EPFL（信号处理实验室（LTS5）洛桑联邦理工学院）
Existing reference (RF)-based super-resolution (SR) models try to improve perceptual quality in SR under the assumption of the availability of high-resolution RF images paired with low-resolution (LR) inputs at testing. As the RF images should be similar in terms of content, colors, contrast, etc. to the test image, this hinders the applicability in a real scenario. Other approaches to increase the perceptual quality of images, including perceptual loss and adversarial losses, tend to dramatically decrease fidelity to the ground-truth through significant decreases in PSNR/SSIM. Addressing both…
Evaluating Vision-Language Models for Image Quality Assessment using Psychophysical Data
School of Engineering, University of Galway（Galway大学工程学院）；State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University（浙江大学极端光信息获取国家重点实验室）
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
Universität Bremen（不来梅大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Berlin Institute of Technology（柏林工业大学）
Samsung AI Center（三星人工智能中心）；University of Warwick（华威大学）；University of Cambridge（剑桥大学）
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks. However, these convolutional models are excessively large and expensive, hindering their effective deployment to end devices. In this work, we propose a neural architecture search (NAS) approach that integrates NAS and generative adversarial networks (GANs) with recent advances in perceptual SR and pushes the efficiency of small perceptual SR models to facilitate on-device execution. Specifically, we search over the …
We introduce Frequency Domain Perceptual Loss (FDPL), a loss function for single image super resolution (SR). Unlike previous loss functions used to train SR models, which are all calculated in the pixel (spatial) domain, FDPL is computed in the frequency domain. By working in the frequency domain we can encourage a given model to learn a mapping that prioritizes those frequencies most related to human perception. While the goal of FDPL is not to maximize the Peak Signal to Noise Ratio (PSNR), we found that there is a correlation between decreasing FDPL and increasing PSNR. Training a model wi…
Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost. However, we observe that previous unpaired image dehazing approaches tend to suffer from performance degradation near depth borders, where depth tends to vary abruptly. Hence, we propose to anneal the depth border degradation in unpaired image dehazing with cyclic perceptual-depth supervision. Coupled with the dual-path feature re-using backbones of the generators and discriminators, our model achieves $\mathbf{20.36}$ Peak Signal-to-Noise Ratio (PSNR) on NYU Dept…
Super-resolution results are usually measured by full-reference image quality metrics or human rating scores. However, these evaluation methods are general image quality measurement, and do not account for the nature of the super-resolution problem. In this work, we analyze the evaluation problem based on the one-to-many mapping nature of super-resolution, and propose a novel distribution-based metric for super-resolution. Starting from the distribution distance, we derive the proposed metric to make it accessible and easy to compute. Through a human subject study on super-resolution, we show …
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
Carnegie Mellon University（卡内基梅隆大学）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；International Institute of Information Technology Bhubaneswar（布巴内斯瓦尔国际信息技术学院）
Liaoning University of Technology（辽宁工业大学）；Dalian University（大连大学）
The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction. Furthermore, the generative adversarial networks (GAN) is applied to the super-resolution field, which effectively improves the visual quality of the reconstructed image. However, under the condtion of high upscaling factors, the excessive abnormal reasoning of the network produces some distorted structures, so that there is a certain deviation between th…
Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to their simplicity and speed. Recent modern deep learning approaches can perform better, but they rely on supervised data which can be costly to gather: large sets of class labels such as ImageNet, image quality ratings, or both. We combine recent advances in information-theoretic objective functions with a computational architecture informed by the physiology of the human visual system and unsupervised training on pa…
Beijing University of Posts and Telecommunications（北京邮电大学）
Conventional image compression methods typically aim at pixel-level consistency while ignoring the performance of downstream AI tasks.To solve this problem, this paper proposes a Semantic-Assisted Image Compression method (SAIC), which can maintain semantic-level consistency to enable high performance of downstream AI tasks.To this end, we train the compression network using semantic-level loss function. In particular, semantic-level loss is measured using gradient-based semantic weights mechanism (GSW). GSW directly consider downstream AI tasks' perceptual results. Then, this paper proposes a…
Perceptually Regularized Diffusion Model for Image Super-Resolution
University of North Carolina（北卡罗来纳大学）；Texas Tech University（德克萨斯理工大学）；Case Western Reserve University（凯斯西储大学）；University of Houston（休斯顿大学）；University of Kentucky（肯塔基大学）
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
School of Software, Dalian University of Technology（大连理工大学软件学院）；International School of Information Science and Engineering, Dalian University of Technology（大连理工大学国际信息与工程学院）
Spatial resolution adaptation is a technique which has often been employed in video compression to enhance coding efficiency. This approach encodes a lower resolution version of the input video and reconstructs the original resolution during decoding. Instead of using conventional up-sampling filters, recent work has employed advanced super-resolution methods based on convolutional neural networks (CNNs) to further improve reconstruction quality. These approaches are usually trained to minimise pixel-based losses such as Mean-Squared Error (MSE), despite the fact that this type of loss metric …
Many super-resolution (SR) models are optimized for high performance only and therefore lack efficiency due to large model complexity. As large models are often not practical in real-world applications, we investigate and propose novel loss functions, to enable SR with high perceptual quality from much more efficient models. The representative power for a given low-complexity generator network can only be fully leveraged by strong guidance towards the optimal set of parameters. We show that it is possible to improve the performance of a recently introduced efficient generator architecture sole…
Perceptual Quality Optimization of Image Super-Resolution
School of Computer Science and Informatics, Cardiff University, UK（计算机科学与信息学学院，卡迪夫大学）；Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität, Klagenfurt, Austria（Christian Doppler实验室ATHENA，阿尔卑斯-亚得里亚大学）；XiaomiEV, China（小米EV，中国）；Centre for Vision, Speech and Signal Processing, University of Surrey, UK（视觉、语音和信号处理中心，萨里大学）
West Virginia University（西弗吉尼亚大学）；Case Western Reserve University（凯斯西储大学）
Image demosaicing and super-resolution are two important tasks in color imaging pipeline. So far they have been mostly independently studied in the open literature of deep learning; little is known about the potential benefit of formulating a joint demosaicing and super-resolution (JDSR) problem. In this paper, we propose an end-to-end optimization solution to the JDSR problem and demonstrate its practical significance in computational imaging. Our technical contributions are mainly two-fold. On network design, we have developed a Residual-Dense Squeeze-and-Excitation Networks (RDSEN) supporte…
Institute of Computing Technology（计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Huawei Technologies（华为技术有限公司）；University of Science and Technology of China（中国科学技术大学）
In recent years, creative content generations like style transfer and neural photo editing have attracted more and more attention. Among these, cartoonization of real-world scenes has promising applications in entertainment and industry. Different from image translations focusing on improving the style effect of generated images, video cartoonization has additional requirements on the temporal consistency. In this paper, we propose a spatially-adaptive semantic alignment framework with perceptual motion consistency for coherent video cartoonization in an unsupervised manner. The semantic align…
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Whirlpool（惠而浦公司）
Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial netwo…
Goldsmiths, University of London（伦敦大学金史密斯学院）；Paul Math School（保罗数学学校）
Recently, there has been discussions on the ill-posed nature of super-resolution that multiple possible reconstructions exist for a given low-resolution image. Using normalizing flows, SRflow[23] achieves state-of-the-art perceptual quality by learning the distribution of the output instead of a deterministic output to one estimate. In this paper, we adapt the concepts of SRFlow to improve GAN-based super-resolution by properly implementing the one-to-many property. We modify the generator to estimate a distribution as a mapping from random noise. We improve the content loss that hampers the p…
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image super-resolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special ster…
University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions and image quality with no extra acquisition costs. In the past few decades, the rapid development of deep neural networks has promoted super-resolution performance with novel network architectures, loss functions and evaluation metrics. Specifically, vision transformers dominate a broad range of computer vision tasks, but challenges still exist when applying them to low-level medical image processing tasks. This paper proposes an efficient vision transformer wi…
National University of Singapore（新加坡国立大学）；HiSilicon Technologies（海思半导体）
In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable. However, most deep learning methods only achieve high performance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this paper, we propose a novel super-resolution model with a low-frequency constraint (LFc-SR), which balances the objective and perceptual quality through a single model and yields super-resolved images with high PSNR and perceptual scores. We furt…
Czech Technical University in Prague（布拉格捷克理工大学）；Amazon Web Services（亚马逊云科技）
This paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a hierarchical auto-regressive model. This paper demonstrates that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of image compression techniques as they do not align with the human perception of similarity. Alternatively, a new metric is proposed, which is learned on perceptual similarity data specific to image comp…
