Results 11 to 20 of about 137,048 (270)
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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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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Deep Blind Video Super-resolution
CoRR, 2020 Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for video SR and usually leads to over-smoothed super-resolved images.
Jinshan Pan +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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Deep Blind Super-Resolution for Satellite Video
IEEE Transactions on Geoscience and Remote Sensing, 2023 Recent efforts have witnessed remarkable progress in Satellite Video Super-Resolution (SVSR). However, most SVSR methods usually assume the degradation is fixed and known, e.g., bicubic downsampling, which makes them vulnerable in real-world scenes with multiple and unknown degradations.
Yi Xiao 0003 +3 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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BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK
Iraqi Journal of Information & Communication Technology, 2021 Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image.
Meryem H. Muhson, Ayad A. Al-Ani
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