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 \url{https://github.com/HnV-Lab/CAIR}.
翻译：在计算机视觉中,图像的恢复是一项重要而艰巨的任务。 将过滤后的图像倒回其原始图像有助于各种计算机视觉任务。 我们使用非线性激活功能免费网络(NAFNet),用于快速和轻量级模型,并添加一个色调关注模块,提取有用的颜色信息,以便提高准确性。 我们提议建立一个准确、快速、轻量化的网络,为Instagram过滤器(CAIR)提供多尺度和彩色关注。 实验结果表明,拟议的CAIR以快速和轻量化的方式超越了现有的Instagram过滤网络,以更快和轻量级方式,大约11美元,2.4美元轻度,同时在FIFI数据集上超过3.69 dB PSNR。 CAIR能够以高质量的方式成功去除Instagram过滤器,并恢复质量上的颜色信息。 源码和预培训重量可在\url{https://github.com/HnV-Lab/CAIR}上查阅。
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