Tan Daniel Stanley, Chen Wei-Yang, Hua Kai-Lung
IEEE Trans Image Process. 2018 Feb 7. doi: 10.1109/TIP.2018.2803341.
Convolutional neural networks are currently the state-of-the-art solution for a wide range of image processing tasks. Their deep architecture extracts low and high-level features from images, thus, improving the model's performance. In this paper, we propose a method for image demosaicking based on deep convolutional neural networks. Demosaicking is the task of reproducing full color images from incomplete images formed from overlaid color filter arrays on image sensors found in digital cameras. Instead of producing the output image directly, the proposed method divides the demosaicking task into an initial demosaicking step and a refinement step. The initial step produces a rough demosaicked image containing unwanted color artifacts. The refinement step then reduces these color artifacts using deep residual estimation and multi-model fusion producing a higher quality image. Experimental results show that the proposed method outperforms several existing and state-of-the-art methods in terms of both subjective and objective evaluations.
卷积神经网络目前是广泛图像处理任务的最先进解决方案。它们的深度架构从图像中提取低级和高级特征，从而提高模型的性能。在本文中，我们提出了一种基于深度卷积神经网络的图像去马赛克方法。去马赛克是从数字相机图像传感器上叠加的彩色滤光片阵列形成的不完整图像中再现全彩色图像的任务。所提出的方法不是直接生成输出图像，而是将去马赛克任务分为初始去马赛克步骤和细化步骤。初始步骤产生包含不需要的颜色伪像的粗糙去马赛克图像。然后，细化步骤使用深度残差估计和多模型融合减少这些颜色伪像，从而产生更高质量的图像。实验结果表明，所提出的方法在主观和客观评估方面均优于几种现有和最先进的方法。
