Image demosaicking and denoising are the first two key steps of the color image production pipeline. The classical processing sequence has for a long time consisted of applying denoising first, and then demosaicking. Applying the operations in this order leads to oversmoothing and checkerboard effects. Yet, it was difficult to change this order, because once the image is demosaicked, the statistical properties of the noise are dramatically changed and hard to handle by traditional denoising models. In this paper, we address this problem by a hybrid machine learning method. We invert the traditional color filter array (CFA) processing pipeline by first demosaicking and then denoising. Our demosaicking algorithm, trained on noiseless images, combines a traditional method and a residual convolutional neural network (CNN). This first stage retains all known information, which is the key point to obtain faithful final results. The noisy demosaicked image is then passed through a second CNN restoring a noiseless full-color image. This pipeline order completely avoids checkerboard effects and restores fine image detail. Although CNNs can be trained to solve jointly demosaicking-denoising end-to-end, we find that this two-stage training performs better and is less prone to failure. It is shown experimentally to improve on the state of the art, both quantitatively and in terms of visual quality.

翻译：图像去马赛克与去噪是彩色图像处理流水线中的前两个关键步骤。经典处理顺序长期以来遵循先进行去噪、再进行去马赛克的流程。这种顺序操作会导致过度平滑和棋盘格效应。然而，改变这一顺序十分困难，因为图像完成去马赛克后，噪声的统计特性会发生显著变化，传统去噪模型难以处理。本文通过混合机器学习方法解决该问题。我们反转了传统彩色滤光片阵列处理流水线，采用先去马赛克后去噪的顺序。我们的去马赛克算法在无噪声图像上训练，结合了传统方法与残差卷积神经网络。这一第一阶段保留了所有已知信息，这是获得忠实最终结果的关键。随后，含噪去马赛克图像通过第二个卷积神经网络恢复为无噪全彩图像。这种流水线顺序完全避免了棋盘格效应，并恢复了精细图像细节。尽管卷积神经网络可通过端到端方式联合训练去马赛克与去噪任务，但研究发现双阶段训练策略表现更优且不易失败。实验表明，该方法在量化指标和视觉质量上均优于现有技术。