Convolutional neural network-based depth image artifact removal
In 3D video coding and depth-based image rendering, the distortion of the compressed depth image often leads to wrong 3D warpping. In this paper, by generalizing the recent work of convolutional neural network (CNN)-based depth image up-sampling, we propose a CNN-based depth image artifact removal scheme, where both the compressed depth and color images are used to enhance the depth accuracy. The proposed CNN has two sub-networks: joint depth-color sub-network and joint depth sub-network. During the depth and color feature extraction, the gradient of the depth image is used as the input to color image, while the gradient of color image is used as the input of depth feature extraction. Such an exchange of gradient information improves the learned features. Experimental results in terms of both objective and subjective quality of the depth and color images verify the efficiency of the proposed method.