2018/04/11 by Bolin Liu, Liu, Bolin, Xiao Shu +3 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Alias #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Convolutional neural network #Data mining #Digital Media Forensic Detection #FOS: Computer and information sciences #Geography #Image (mathematics) #Image and Signal Denoising Methods #Moiré pattern #Multimedia (cs.MM) #Pattern recognition (psychology) #Pixel #Scale (ratio) #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.1804.03809
arxiv created 2018/04/11 · openalex publication_date 2018/04/11 · arxiv updated 2018/04/13 · openalex created_date 2018/04/24 · openalex updated_date 2026/07/28
Taking photos of optoelectronic displays is a direct and spontaneous way of transferring data and keeping records, which is widely practiced. However, due to the analog signal interference between the pixel grids of the display screen and camera sensor array, objectionable moiré (alias) patterns appear in captured screen images. As the moiré patterns are structured and highly variant, they are difficult to be completely removed without affecting the underneath latent image. In this paper, we propose an approach of deep convolutional neural network for demoiréing screen photos. The proposed DCNN consists of a coarse-scale network and a fine-scale network. In the coarse-scale network, the input image is first downsampled and then processed by stacked residual blocks to remove the moiré artifacts. After that, the fine-scale network upsamples the demoiréd low-resolution image back to the original resolution. Extensive experimental results have demonstrated that the proposed technique can efficiently remove the moiré patterns for camera acquired screen images; the new technique outperforms the existing ones.