Demoiréing of Camera-Captured Screen Images Using Deep Convolutional Neural Network
This addresses a common issue in photography for users who capture screen images, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of removing moiré patterns from camera-captured screen images using a deep convolutional neural network, achieving efficient removal and outperforming existing methods.
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.