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Works61 from public data
- Enhanced Deep Residual Networks for Single Image Super-Resolution7,719
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
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- AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network224
This work develops AP-BSN, a state-of-the-art self-supervised denoising method for real-world sRGB images, and proposes random-replacing refinement, which significantly improves the performance of the AP- BSN without any additional parameters.
- 172
- CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image125
This work proposes a novel and powerful self-supervised denoising method called CVF-SID based on a Cyclic multi-Variate Function (CVF) module and a self- supervised image disentangling (SID) framework, which can disentangle a clean image and noise maps from the input by leveraging various self- Supervised loss terms.
- C2N: Practical Generative Noise Modeling for Real-World Denoising121
A Clean-to-Noisy image generation framework, namely C2N, to imitate complex real-world noise without using any paired examples, and conventional denoising CNNs can be trained to outperform existing unsupervised methods on challenging real- world benchmarks by a large margin.
- NTIRE 2021 Challenge on Image Deblurring106
The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts.
- Clustering Convolutional Kernels to Compress Deep Neural Networks89
A novel method to compress CNNs by reconstructing the network from a small set of spatial convolution kernels using k-means clustering, which outperforms its uncompressed counterpart at ILSVRC2012 classification task with over 10x compression ratio.
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- SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation48
This work interprets the traditional image warping task, specifically when the input is enlarged, as a spatially-varying SR problem and proposes several novel formulations, including the adaptive warping layer and multiscale blending, to reconstruct visually favorable results in the transformation process.
- Toward Real-World Super-Resolution via Adaptive Downsampling Models48
This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge and proposes a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples.
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- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- NTIRE 2020 Challenge on Image and Video Deblurring35
This challenge presents the evaluation results from 3 competition tracks as well as the proposed solutions, and the winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks.
- AIM 2020 Challenge on Video Temporal Super-Resolution22
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis, and proposes the enhanced quadratic video interpolation method.
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- AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results20
This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results, and employs the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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