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IEEE Workshop/Winter Conference on Applications of Computer Vision
Single image super-resolution (SISR) for reconstructing from a low-resolution (LR) input image its corresponding high-resolution (HR) output is a widely-studied research problem in the field of multimedia applications and computer vision.
Shao-Yu Weng +4 more
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Single image super-resolution (SISR) for reconstructing from a low-resolution (LR) input image its corresponding high-resolution (HR) output is a widely-studied research problem in the field of multimedia applications and computer vision.
Shao-Yu Weng +4 more
semanticscholar +1 more source
Simpler Gradient Methods for Blind Super-Resolution With Lower Iteration Complexity
IEEE Transactions on Signal ProcessingWe study the problem of blind super-resolution, which can be formulated as a low-rank matrix recovery problem via vectorized Hankel lift (VHL). The previous gradient descent method based on VHL named PGD-VHL relies on additional regularization such as ...
Jinsheng Li, Wei Cui, Xu Zhang
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IDENet: Implicit Degradation Estimation Network for Efficient Blind Super Resolution
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Blind image super-resolution (SR) aims to recover high-resolution (HR) images from low-resolution (LR) inputs hindered by unknown degradation. Existing blind SR methods exploit computationally demanding explicit degradation estimators hinging on the ...
Asif Hussain Khan +2 more
semanticscholar +1 more source
IEEE journal of biomedical and health informatics
Cardiac magnetic resonance imaging (CMRI) super-resolution (SR) reconstruction technology can enhance the resolution and quality of CMRI, providing experts with clearer and more accurate information about cardiac structure and function.
Zhaoyang Song +5 more
semanticscholar +1 more source
Cardiac magnetic resonance imaging (CMRI) super-resolution (SR) reconstruction technology can enhance the resolution and quality of CMRI, providing experts with clearer and more accurate information about cardiac structure and function.
Zhaoyang Song +5 more
semanticscholar +1 more source
Lightweight Prompt Learning Implicit Degradation Estimation Network for Blind Super Resolution
IEEE Transactions on Image ProcessingBlind image super-resolution (SR) aims to recover a high-resolution (HR) image from its low-resolution (LR) counterpart under the assumption of unknown degradations.
Asif Hussain Khan +2 more
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Degradation Regression with Uncertainty for Blind Super-Resolution
Neurocomputing, 2023Shang Li +5 more
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International Conference on Climate Informatics
In the field of single image super‐resolution, the prevalent use of convolutional neural networks (CNN) typically assumes a simplistic bicubic downsampling model for image degradation.
Yinghua Li +6 more
semanticscholar +1 more source
In the field of single image super‐resolution, the prevalent use of convolutional neural networks (CNN) typically assumes a simplistic bicubic downsampling model for image degradation.
Yinghua Li +6 more
semanticscholar +1 more source
Two Heads Better Than One: Dual Degradation Representation for Blind Super-Resolution
International Conference on Information PhotonicsPrevious methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from these assumptions, these methods may
Hsuan Yuan +7 more
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Reference-Based Blind Super-Resolution Kernel Estimation
2022 IEEE International Conference on Image Processing (ICIP), 2022Mehmet Yamac, Aakif Nawaz, Baran Ataman
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