Image demosaicking and denoising are the two key steps for color image
production pipeline. The classical processing sequence consists of applying
denoising first, and then demosaicking. However, this sequence leads to
oversmoothing and unpleasant checkerboard effect. Moreover, it is very
difficult to change this order, because once the image is demosaicked, the
statistical properties of the noise will be changed dramatically. This is
extremely challenging for traditional denoising models that strongly rely on
statistical assumptions. In this paper, we attempt to tackle this prickly
problem. Indeed, here we invert the traditional CFA processing pipeline by
first demosaicking and then denoising. In the first stage, we design a
demosaicking algorithm that combines traditional methods and a convolutional
neural network (CNN) to reconstruct a full color image ignoring the noise. To
improve the performance in image demosaicking, we modify an Inception
architecture for fusing R, G and B three channels information. This stage
retains all known information that is the key point to obtain pleasurable final
results. After demosaicking, we get a noisy full-color image and use another
CNN to learn the demosaicking residual noise (including artifacts) of it, that
allows to obtain a restored full color image. Our proposed algorithm completely
avoids the checkerboard effect and retains more image detail. Furthermore, it
can process very high-level noise while the performances of other CNN based
methods for noise higher than 20 are rather limited. Experimental results show
clearly that our method outperforms state-of-the-art methods both
quantitatively as well as in terms of visual quality.

本文提出了一种优于当前最先进方法的图像去马赛克和降噪算法，该算法将传统的 CFA 处理流程中的处理顺序颠倒，首先进行图像去马赛克，然后进行降噪，避免了传统方法中的众多问题。算法利用卷积神经网络进行实现并具有处理高噪声的能力。