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Self-Supervised Deep Blind Video Super-Resolution [article]

Haoran Bai, Jinshan Pan
2022 arXiv   pre-print
Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or  ...  To overcome these problems, we propose a self-supervised learning method to solve the blind video SR problem, which simultaneously estimates blur kernels and HR videos from the LR videos.  ...  Index Terms—Self-supervised learning, blind video super-resolution, convolutional neural network, deep learning.  ... 
arXiv:2201.07422v1 fatcat:nufcy5tpbjaolnacrfhs7gokl4

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
., +, TIP 2021 9208-9219 Heavy-Tailed Self-Similarity Modeling for Single Image Super Resolution.  ...  Model-Guided Deep Hyperspectral Image Super-Resolution. Dong, W., +, Progressive Diversified Augmentation for General Robustness of DNNs: A spectral Image Super-Resolution.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

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  ...  images, which represents the combination of noisy, blurred and low-resolution images.  ...  It is utilized in image classification, denoising and super-resolution, and video tracking.  ... 
arXiv:1912.13171v4 fatcat:4ts2xpivhreptelbgeqhljjiri

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Spatiotemporal Guided Self-Supervised Depth Completion from LiDAR and Monocular Camer Wang, Huairui DOVE: Decomposition Oriented Video super- rEsolution Wang, I-Hsiang Deep Learning Based EBCOT  ...  super- rEsolution Yang, Daiqin Drone-Based Car Counting via Density Map Learning Yang, Kun Deep Learning-Based Nonlinear Transform for HEVC Intra Coding Yang, Le Fast Video Saliency Detection  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Nazir, A., Brain Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors.  ...  ., +, TIP 2020 2380-2394 Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of contents

2020 IEEE Transactions on Image Processing  
Chen 4027 Learning a Deep Dual Attention Network for Video Super-Resolution ...................... F. Li, H. Bai, and Y.  ...  Gao 1725 Deep Coupled ISTA Network for Multi-Modal Image Super-Resolution ............... X. Deng and P.  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
Li, J., +, TMM 2021 1397-1409 Supervised Pixel-Wise GAN for Face Super-Resolution.  ...  Wang, Q., +, TMM 2021 429-442 Supervised Pixel-Wise GAN for Face Super-Resolution.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Table of Contents

2021 2021 IEEE International Conference on Image Processing (ICIP)  
COLOR MISMATCH .... 1784 Oguzhan Ulucan, Diclehan Karakaya, Mehmet Turkan, Izmir University of Economics, Turkey TEC-4: SUPER-RESOLUTION TEC-4.1: DEEP BLIND UN-SUPERVISED LEARNING NETWORK FOR SINGLE  ...  ...................................................... 1789 IMAGE SUPER RESOLUTION Kazuhiro Yamawaki, Xian-Hua Han, Yamaguchi University, Japan TEC-4.3: SINGLE IMAGE SUPER-RESOLUTION VIA GLOBAL-CONTEXT  ... 
doi:10.1109/icip42928.2021.9506758 fatcat:5g2bwdt2efafjd2mubhxyv4m4y

Deep Learning for Image/Video Restoration and Super-resolution

A. Murat Tekalp
2022 Foundations and Trends in Computer Graphics and Vision  
Recent advances in neural signal processing led to significant improvements in the performance of learned image/video restoration and super-resolution (SR).  ...  An important benefit of data-driven deep learning approaches to image processing is that neural models can be optimized for any differentiable loss function, including perceptual loss functions, leading  ...  Introduction Deep learning has made a significant impact not only on computer vision and natural language processing but also on classical signal processing problems such as image/video restoration/super-resolution  ... 
doi:10.1561/0600000100 fatcat:5keqxf3lingubhlgrptdpq42xy

Table of Contents

2021 IEEE Signal Processing Letters  
Lu Reconciling Hand-Crafted and Self-Supervised Deep Priors for Video Directional Rain Streaks Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Huang Deep Bilateral Learning for Stereo Image Super-Resolution . . . . . . . . . . Q. Xu, L. Wang, Y. Wang, W. Sheng, and X.  ... 
doi:10.1109/lsp.2021.3134551 fatcat:ab4b4tb5rrcu5cq6aifdekrizq

Generative Adversarial Networks for Image Super-Resolution: A Survey [article]

Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, Yanning Zhang
2022 arXiv   pre-print
Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised  ...  Single image super-resolution (SISR) has played an important role in the field of image processing.  ...  In terms of remote sensing image super-resolution, Gong et al. used enlighten blocks to make a deep network achieve a reliable point and used self-supervised hierarchical perceptual loss to overcome effects  ... 
arXiv:2204.13620v1 fatcat:hlwdqith65cxrbqrnbphjz6u4u

Table of contents

2020 IEEE Transactions on Image Processing  
Huang 4461 Learning a Deep Dual Attention Network for Video Super-Resolution ...................... F. Li, H. Bai, and Y.  ...  Tao 1669 Deep Coupled ISTA Network for Multi-Modal Image Super-Resolution ............... X. Deng and P.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Table of Contents

2021 IEEE transactions on multimedia  
Ngan Accurate and Efficient Image Super-Resolution via Global-Local Adjusting Dense Network . . . . . . . . . . . . . . . . . . . . . . . . .Supervised Pixel-Wise GAN for Face Super-Resolution . . . .  ...  Fang Image/Video/Graphics Analysis and Synthesis Deep Unsupervised Binary Descriptor Learning Through Locality Consistency and Self Distinctiveness . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tmm.2021.3132246 fatcat:el7u2udtybddrpbl5gxkvfricy

Online Learning for Reference-Based Super-Resolution

Byungjoo Chae, Jinsun Park, Tae-Hyun Kim, Donghyeon Cho
2022 Electronics  
Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks.  ...  Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input  ...  Introduction Deep learning-based single-image super-resolution (SISR) algorithms [1] [2] [3] [4] [5] [6] [7] [8] [9] have shown remarkable progress in recent years.  ... 
doi:10.3390/electronics11071064 fatcat:amgcsmp5gbajxe2hghkw4p66oi

Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks [article]

Alice Lucas, Santiago Lopez-Tapia, Rafael Molina, Aggelos K. Katsaggelos
2020 arXiv   pre-print
While Convolutional Neural Networks (CNNs) trained for image and video super-resolution (SR) regularly achieve new state-of-the-art performance, they also suffer from significant drawbacks.  ...  While the Deep Learning literature focuses on presenting new training schemes and settings to resolve these various issues, we show that one can avoid training and correct for SR results with a fully self-supervised  ...  Self-supervised fine-tuning for correcting SR CNNs Suppose access to a super-resolving CNN f θ (·) trained on a large dataset (X , Y ) with objective function C .  ... 
arXiv:1912.12879v3 fatcat:dxlaxj75kjhnnnw7pm4izvwayy
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