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Works19 from public data
- 110
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
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- NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results43
The NTIRE2021 challenge on burst super-resolution was to generate a clean RGB image with 4 times higher resolution given a RAW noisy burst as input, and the top-performing methods set a new state-of-the-art for the burstsuper-resolution task.
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- AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results24
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020 and gauges the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM.
- 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.
- Context-Aware Region-Dependent Scale Proposals for Scale-Optimized Object Detection Using Super-Resolution16
Qualitative and quantitative experiments show that the RDSP network provides appropriate SR scales and improve detection accuracy on highly challenging dataset, captured by real car-mounted cameras with size-varied objects, including extremely small objects.
- Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality6
The proposed method uses burst LR features to reconstruct the initial burst SR image that is fed into an intermediate step in the diffusion model, and skips diffusion steps for reconstructing the global structure of the image and focuses on steps for refining detailed textures.
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- Test-Time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution With Variable Fourier Components3
This work proposes a novel SR method using a Recurrent Neural Network (RNN) with the Fourier representation, which achieves a lower PSNR drop than other state-of-the-art arbitrary-scale SR methods.
- Neural image enhancement and restoration for time-lapse SPM images3
These proposed methods using a recurrent neural network improve the enhancement and restoration of SPM images by merging the time-lapse images in order to acquire a single enhanced/restored image.
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- Efficient Burst Super-Resolution with One-Step Diffusion2
This work aims to reconstruct sharp and high-fidelity SR images by a diffusion model with a stochastic sampler with a high-order ODE as well as one-step diffusion using knowledge distillation to reduce the runtime and maintain the SR quality measured based on image distortion and perceptual quality.
- Kernelized Back-Projection Networks for Blind Super Resolution2
It is revealed that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR.
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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 2026-10-14. 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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