AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
October 17, 2022 Β· Declared Dead Β· π ECCV Workshops
"No code URL or promise found in abstract"
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Authors
Furkan KΔ±nlΔ±, Sami MenteΕ, BarΔ±Ε Γzcan, Furkan KΔ±raΓ§, Radu Timofte, Yi Zuo, Zitao Wang, Xiaowen Zhang, Yu Zhu, Chenghua Li, Cong Leng, Jian Cheng, Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Tianzhi Ma, Zihan Gao, Wenxin He, Woon-Ha Yeo, Wang-Taek Oh, Young-Il Kim, Han-Cheol Ryu, Gang He, Shaoyi Long, S. M. A. Sharif, Rizwan Ali Naqvi, Sungjun Kim, Guisik Kim, Seohyeon Lee, Sabari Nathan, Priya Kansal
arXiv ID
2210.08997
Category
cs.CV: Computer Vision
Cross-listed
cs.LG,
eess.IV
Citations
10
Venue
ECCV Workshops
Last Checked
4 months ago
Abstract
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.
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