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AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

17 October 2022
Furkan Kinli
Sami Mentecs
Barics Ozcan
Furkan Kiracc
Radu Timofte
Yihao Zuo
Zitao Wang
Xiaowen Zhang
Yu Zhu
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. Sharif
R. A. Naqvi
Sungjun Kim
Guisik Kim
Seohyeon Lee
S. Nathan
Priya Kansal
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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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