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.

本文介绍了 AIM 2022 挑战赛关于 Instagram 滤镜去除的方法和结果，对应用深度学习策略、去除滤镜产生负面影响的问题进行了研究和解决，基于 PSNR 指标对 9 个团队提出的方案进行了比较分析，得出定性结果。

2022 年 AIM 挑战赛：Instagram 滤镜去除方法与结果

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Image restoration is an important and challenging task in computer vision.
Reverting a filtered image to its original image is helpful in various computer
vision tasks. We employ a nonlinear activation function free network (NAFNet)
for a fast and lightweight model and add a color attention module that extracts
useful color information for better accuracy. We propose an accurate, fast,
lightweight network with multi-scale and color attention for Instagram filter
removal (CAIR). Experiment results show that the proposed CAIR outperforms
existing Instagram filter removal networks in fast and lightweight ways, about
11$\times$ faster and 2.4$\times$ lighter while exceeding 3.69 dB PSNR on IFFI
dataset. CAIR can successfully remove the Instagram filter with high quality
and restore color information in qualitative results. The source code and
pretrained weights are available at https://github.com/HnV-Lab/CAIR.

提出了一个准确、快速、轻量级的网络 CAIR 用于 Instagram 滤镜去除，采用非线性激活函数自由网络（NAFNet）和加入颜色注意力模块，达到 11× 快速和 2.4× 轻量级，同时在 IFFI 数据集上超过了 3.69 dB PSNR，并成功去除了 Instagram 滤镜和恢复了色彩信息。