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。