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Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution [chapter]

Ahmed Cheikh Sidiya, Xin Li
2020 Recent Advances in Image Restoration with Applications to Real World Problems  
For real-world low-resolution (LR) face images, we propose a novel unsupervised learning approach by combining style-based generator with relativistic discriminator.  ...  However, similar success has not been witnessed in related areas such as face single image super-resolution (SISR).  ...  Single image super-resolution using generative adversarial networks In SRGAN [2] , the authors showed that using a GAN-based architecture for the task of single image super-resolution leads to noticeable  ... 
doi:10.5772/intechopen.92320 fatcat:vgjripwupvbyniewlxyffznyty

Learning Invariant Representation for Unsupervised Image Restoration [article]

Wenchao Du, Hu Chen, Hongyu Yang
2020 arXiv   pre-print
Recently, cross domain transfer has been applied for unsupervised image restoration tasks.  ...  Instead, we propose an unsupervised learning method that explicitly learns invariant presentation from noisy data and reconstructs clear observations.  ...  Recently, deep neural networks (DNNs) and generative adversarial networks (GANs) [10] have shown their superior performance in various lowlevel vision tasks.  ... 
arXiv:2003.12769v1 fatcat:rdjmgqc2rrgtxo62f34ioft2lq

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Direct Unsupervised Super-Resolution Using Generative Adversarial Network (DUS-GAN) for Real-World Data.  ...  ., +, TIP 2021 2682-2696 Direct Unsupervised Super-Resolution Using Generative Adversarial Net-TIP 2021 9030-9042 Data models DotFAN: A Domain-Transferred Face Augmentation Net.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models [article]

Rui Ma, Johnathan Czernik, Xian Du
2021 arXiv   pre-print
Most single image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs, which are simulated by a predetermined degradation operation, e.g.,  ...  However, these methods only learn the inverse process of the predetermined operation, so they fail to super resolve the real-world LR images; the true formulation deviates from the predetermined operation  ...  Once this is achieved, the output of this network is used to train a low-to-high GAN for image super-resolution, using paired low-and high-resolution images.  ... 
arXiv:2110.10755v1 fatcat:m42bndrminhdpcsktrjv7qt3pa

Improving Shape Deformation in Unsupervised Image-to-Image Translation [article]

Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin
2019 arXiv   pre-print
Inspired by semantic segmentation, we introduce a discriminator with dilated convolutions that is able to use information from across the entire image to train a more context-aware generator.  ...  Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.  ...  This can be accomplished via deep learning with generative adversarial networks (GANs), through the use of a discriminator network to provide instance-specific generator training, and the use of a cyclic  ... 
arXiv:1808.04325v2 fatcat:t6et2bqiiramzmrh3g6ho4pftq

Table of contents

2020 IEEE Transactions on Image Processing  
Li 1493 Multiple Cycle-in-Cycle Generative Adversarial Networks for Unsupervised Image Super-Resolution .................. ...........................................................................  ...  Yang 7153 MEF-GAN: Multi-Exposure Image Fusion via Generative Adversarial Networks .... H. Xu, J. Ma, and X.-P. Zhang 7203 A Local Flatness Based Variational Approach to Retinex ..... M. Tang, F.  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

Table of contents

2020 IEEE Transactions on Image Processing  
Qin 1090 Multiple Cycle-in-Cycle Generative Adversarial Networks for Unsupervised Image Super-Resolution .................. ...........................................................................  ...  Rosin 5408 STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing ................ .............................................................................  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal [article]

Yiyang Shen, Yongzhen Wang, Mingqiang Wei, Honghua Chen, Haoran Xie, Gary Cheng, Fu Lee Wang
2022 arXiv   pre-print
poorly to the real-world scenes.  ...  function to make the model not limited to synthetic datasets but generalize smoothly to real-world heavy rainy scenes.  ...  Acknowledgements The work was supported by the Hong Kong Centre for Logistics Robotics.  ... 
arXiv:2204.13420v1 fatcat:54lqqtmnxbcxtae5cfujfafamu

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TMM 2021 1581-1591 Recurrent Generative Adversarial Network for Face Completion. Wang, Q., +, TMM 2021 429-442 Supervised Pixel-Wise GAN for Face Super-Resolution.  ...  RealVAD: A Real-World Dataset and A Method for Voice Activity Detection by Body Motion Analysis. Beyan, C., +, TMM 2021 2071-2085 Recurrent Generative Adversarial Network for Face Completion.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities [article]

Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo, Gui-Song Xia
2020 arXiv   pre-print
, and (3) Generative Adversarial Network-based scene classification methods.  ...  In addition, we introduce the benchmarks used for scene classification and summarize the performance of more than two dozens of representative algorithms on three commonly-used benchmark data sets.  ...  As a promising unsupervised learning method, generative adversarial networks have been used for tracking scene classification with data sets that lack annotations [73] , [74] , [133] .  ... 
arXiv:2005.01094v1 fatcat:qz3at3gyvrbtzkluumalvpqb64

License Plate Image Reconstruction Based on Generative Adversarial Networks

Mianfen Lin, Liangxin Liu, Fei Wang, Jingcong Li, Jiahui Pan
2021 Remote Sensing  
In this paper, a super-resolution image reconstruction method based on Generative Adversarial Networks (GAN) is proposed.  ...  The proposed method mainly consists of four parts: (1) pretreatment for the input image; (2) image features extraction using residual dense network; (3) introduction of progressive sampling, which can  ...  Reasons for Using GAN When the probability distribution of real data is difficult to calculate, for example, the input is various pictures of the real world, traditional generative models cannot be directly  ... 
doi:10.3390/rs13153018 fatcat:f6jk4c5etfcs3pwl5ceug4mpaa

CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry [article]

Sungjun Lim, Hyoungjun Park, Sang-Eun Lee, Sunghoe Chang, Jong Chul Ye
2020 arXiv   pre-print
In this paper, we present a novel unsupervised cycle-consistent generative adversarial network (cycleGAN) with a linear blur kernel, which can be used for both blind- and non-blind image deconvolution.  ...  Experimental results using simulated and real experimental data confirm the efficacy of the algorithm.  ...  Real Microscopy Experiments We also used epifluorescence (EPF) data to validate our model with real-world data.  ... 
arXiv:1908.09414v3 fatcat:asmp67cmqvdlfopbutusoa36a4

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review [article]

Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot
2022 arXiv   pre-print
Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion.  ...  Finally, the remaining challenges and potential future directions are highlighted.  ...  the fusion of MS and Pan to generate a high spatial resolution Pan image. In general, AE, CNN, and GAN are commonly-used network architectures for DL-based pansharpening. .  ... 
arXiv:2205.01380v1 fatcat:5btxnj5e5rf2xn65iofrh4epbu

Deep Learning on Image Denoising: An overview [article]

Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, Chia-Wen Lin
2020 arXiv   pre-print
We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy  ...  Finally, we point out some potential challenges and directions of future research.  ...  [262] proposed to use cascaded deblurring and singleimage super-resolution (SISR) networks to recover plug-and-play super-resolution images.  ... 
arXiv:1912.13171v4 fatcat:4ts2xpivhreptelbgeqhljjiri

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
396 A General Framework for Small Object Detection Leveraging on Simultaneous Unsupervised Super-resolution Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data DAY 4  ...  : Real-World Adversarial Attack on ArcFace Face ID System DAY 2 -Jan 13, 2021 Sun, Linhui; Zhang, Yifan; Cheng, Jian; Lu, Hanqing 503 PEAN: 3D Hand Pose Estimation Adversarial Network DAY 2 -Jan  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm
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