Joint Image Denoising and Colorization Using Deep Network

This paper significantly enhances from the work [1] and proposes a deep neural network that solves the denoising and colorization problem simultaneously. The joint problem is solved by two separate sub-networks that are trained in an end-to-end manner. Specifically, map attention modules are used to revise feature maps, while a few convolutional layers to extract features at the beginning of the network helps to boost the proposed network significantly. We use KITTI dataset to prepare training and testing datasets. In addition, we compare the proposed method with the baseline method using the PSNR and SSIM metrics. To have a fair comparison, we train the proposed and baseline methods using the same dataset, loss function, and training configurations. The experimental results show that the proposed method performed significantly better the baseline method in the KITTI dataset.