In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to different disparities. The MVCNN is trained to process each 3DFIS and generate a denoised image stack that contains the recovered image information for regions of particular disparities. The denoised image stacks are then fused together to produce a denoised target view image using the estimated disparity map. Different from conventional multi-view denoising approaches that group similar patches first and then perform denoising on those patches, our CNN-based algorithm saves the effort of exhaustive patch searching and greatly reduces the computational time. In the proposed MVCNN, residual learning and batch normalization strategies are also used to enhance the denoising performance and accelerate the training process. Compared with the state-of-the-art single image and multi-view denoising algorithms, experiments show that the proposed CNN-based algorithm is a highly effective and efficient method in Gaussian denoising of multi-view images.

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