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- Fast and Accurate Single Image Super-Resolution via Information Distillation Network955
This work proposes a deep but compact convolutional network to directly reconstruct the high resolution image from the original low resolution image and demonstrates that the proposed method is superior to the state-of-the-art methods, especially in terms of time performance.
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- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
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- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report69
The target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results.
- Progressive perception-oriented network for single image super-resolution67
This paper proposes a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network and explores a new generator that adopts multi-scale hierarchical features fusion.
- AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results63
This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
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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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