Results 21 to 30 of about 216,992 (320)
Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
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Flow-based Kernel Prior with Application to Blind Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2021 Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization ...
Jingyun Liang +4 more
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Medical image blind super‐resolution based on improved degradation process
IET Image Processing, 2023 Clinical diagnosis has high requirements for the resolution of medical images, but most existing medical images super‐ resolution (SR) methods are performed under a known or specific degradation kernel.
Dangguo Shao +4 more
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AI, 2023 Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL), they do not usually design evaluations with high scaling factors.
Valdivino Alexandre de Santiago Júnior
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Super-Resolution Blind Channel Modeling [PDF]
2011 IEEE International Conference on Communications (ICC), 2011 In this work, we propose a super-resolution blind channel modeling algorithm to characterize wide-band channels comprised of disjoint frequency subbands. Since sounding signals are not available over the frequency guard bands separating adjacent subbands, conventional channel modeling methods suffer from poor performance in modeling the channel ...
Man-On Pun +3 more
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Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
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DynaVSR: Dynamic Adaptive Blind Video Super-Resolution [PDF]
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021 Most conventional supervised super-resolution (SR) algorithms assume that low-resolution (LR) data is obtained by downscaling high-resolution (HR) data with a fixed known kernel, but such an assumption often does not hold in real scenarios. Some recent blind SR algorithms have been proposed to estimate different downscaling kernels for each input LR ...
Lee, Suyoung +2 more
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Zero-Shot Blind Learning for Single-Image Super-Resolution
Information, 2023 Deep convolutional neural networks (DCNNs) have manifested significant performance gains for single-image super-resolution (SISR) in the past few years.
Kazuhiro Yamawaki, Xian-Hua Han
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Opto-Electronic Advances, 2023 Structured illumination microscopy (SIM) is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity.
Elizabeth Abraham +2 more
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Blind Image Quality Assessment for Super Resolution via Optimal Feature Selection
IEEE Access, 2020 Methods for image Super Resolution (SR) have started to benefit from the development of perceptual quality predictors that are designed for super resolved images.
Juan Beron +2 more
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