REAL-WORLD IMAGE SUPER-RESOLUTION VIA KERNEL AUGMENTATION AND STOCHASTIC VARIATION

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Deep learning (DL) based single image super-resolution (SISR) algorithms have now achieved highly satisfactory evaluation and visualization results on synthetic datasets. However, in some practical applications, especially when restoring some real-world low-resolution (LR) photos, the limitation and unicity of the most commonly used bicubic down-sampling kernel often lead to significant performance degradation of models trained under ideal conditions. Thus, we first propose a kernel augmentation (KA) strategy based on generative adversarial networks (GANs) to improve the generalization ability and robustness of current SISR models. Then, we intend to reconstruct the stochastic variation (SV) features that are widely present in natural images to obtain a more realistic feature representation. In the end, extensive experiments demonstrate the feasibility and effectiveness of our approach in dealing with real-world SISR problems.

源语言英语
主期刊名2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
出版商IEEE Computer Society
2506-2510
页数5
ISBN(电子版)9781665496209
DOI
出版状态已出版 - 2022
活动29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, 法国
期限: 16 10月 202219 10月 2022

出版系列

姓名Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

会议

会议29th IEEE International Conference on Image Processing, ICIP 2022
国家/地区法国
Bordeaux
时期16/10/2219/10/22

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