论文标题
CAIR：快速，轻巧的多尺度色彩注意网络，用于删除Instagram滤波器
CAIR: Fast and Lightweight Multi-Scale Color Attention Network for Instagram Filter Removal
论文作者
论文摘要
图像恢复是计算机视觉中的一项重要且具有挑战性的任务。将过滤的图像恢复到其原始图像中有助于各种计算机视觉任务。我们采用非线性激活函数网络（NAFNET）进行快速且轻巧的模型，并添加色彩注意模块，以提取有用的颜色信息，以提高准确性。我们提出了一个准确，快速，轻巧的网络，具有多尺度和色彩的关注，以进行Instagram滤波器删除（CAIR）。实验结果表明，所提出的CAIR以快速和轻巧的方式优于现有的Instagram滤清器删除网络，约11 $ \ times $快速，2.4 $ \ times $ imper，同时超过IFFI数据集上的3.69 db psnr。 CAIR可以成功地删除具有高质量的Instagram过滤器，并以定性结果恢复颜色信息。源代码和预估计的权重可在\ url {https://github.com/hnv-lab/cair}上获得。
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}.
