Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the inter-channel correlation (higher correlation for red/green and green/blue channels than for red/blue) in color images and general characteristics (green channel always shows the best image quality among the three color components) of distorted color images, in this paper, a three-stage convolutional neural network (CNN) structure is proposed for color image restoration tasks. Since the green channel is found to have the best quality among all three channels, in the first stage, the network is designed to reconstruct the green component. Then, with the guidance of the reconstructed green channel from the first stage, the red and blue channels are reconstructed in the second stage with two parallel networks. Finally, the intermediate reconstructions from the previous stages are concatenated and further refined jointly. We demonstrate the capabilities of the proposed three-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising. In addition, we integrate pixel-shuffle convolution into our scheme to improve the efficiency, and also introduce a quality-blind training strategy to simplify the training process for the compression artifacts reduction task. Extensive experimental results and analyses show that the proposed structure successfully exploits the spatial and inter-channel correlation of color images and outperforms the state-of-the-art image reconstruction approaches.

中文翻译：

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利用3阶段CNN进行通道间相关性的彩色图像恢复

图像恢复是图像处理管道和低级计算机视觉任务的关键组成部分。常规的图像恢复方法主要基于手工图像先验。彩色图像的通道间相关没有得到充分利用。受到彩色图像中通道间相关性的特殊特性（红色/绿色和绿色/蓝色通道的相关性高于红色/蓝色的相关性）和一般特性（绿色通道始终显示三种颜色分量中最佳的图像质量）的启发针对失真的彩色图像，本文提出了一种三级卷积神经网络（CNN）结构用于彩色图像的恢复任务。由于发现绿色通道在所有三个通道中质量最高，因此在第一阶段，该网络旨在重建绿色组件。然后，在第一阶段重建的绿色通道的引导下，第二阶段利用两个并行网络重建红色和蓝色通道。最后，将先前阶段的中间重构连接起来并进一步完善。我们用三种典型的彩色图像恢复任务演示了建议的三阶段结构的功能：彩色图像去马赛克，彩色压缩伪像减少以及真实世界的彩色图像去噪。另外，我们将像素混洗卷积集成到我们的方案中以提高效率，并且还引入了质量盲训练策略以简化压缩伪像减少任务的训练过程。