AIM 2020 Challenge on Learned Image Signal Processing Pipeline
| Autor: | Zhihong Pan, Magauiya Zhussip, Jun Jiang, Yu Zhu, Umapada Pal, Yeskendir Koishekenov, Joonyoung Song, Ming Liu, Jinwei Gu, Jun Chen, Chengqi Li, Xin Liu, Xiaohong Liu, Pengliang Tan, Bingxin Hou, Linhui Dai, Bhavya Vasudeva, Jiawei Zhang, Byung-Hoon Kim, Radu Timofte, JaeHyun Baek, Kai Li, Chenghua Li, Sijie Ren, Andrey Ignatov, Puneesh Deora, Haolin Wang, Xueying Hu, Tian Liang, Zhenyu Guo, Cong Leng, Hwechul Cho Ye, Wangmeng Zuo, Baopu Li, Zhilu Zhang, Zhanglin Peng, Yuichi Ito, Jong Chul Ye, Ruimao Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
Demosaicing
business.industry Computer science media_common.quotation_subject Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Color balance Fidelity Pipeline (software) Task (project management) Image (mathematics) Metric (mathematics) Computer vision Artificial intelligence business media_common |
| Zdroj: | Computer Vision – ECCV 2020 Workshops ISBN: 9783030670696 ECCV Workshops (3) |
| Popis: | This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB mapping problem, where to goal was to map the original low-quality RAW images captured by the Huawei P20 device to the same photos obtained with the Canon 5D DSLR camera. The considered task embraced a number of complex computer vision subtasks, such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions’ perceptual results measured in a user study. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical image signal processing pipeline modeling. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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