Demoir\'eing of Camera-Captured Screen Images Using Deep Convolutional Neural Network

TL;DR

This paper introduces a deep convolutional neural network approach with coarse and fine scales to effectively remove moiré patterns from camera-captured screen images, outperforming existing methods.

Contribution

The paper proposes a novel DCNN architecture with a two-scale process specifically designed for demoiréing camera-captured screen images, improving removal quality.

Findings

Efficient removal of moiré patterns demonstrated.

Outperforms existing demoiréing techniques.

Effective in preserving underlying image details.

Abstract

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 moir\'e (alias) patterns appear in captured screen images. As the moir\'e 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 demoir\'eing 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 moir\'e artifacts. After that, the fine-scale network upsamples the demoir\'ed low-resolution image back to the original resolution. Extensive experimental results have demonstrated that the proposed technique can efficiently remove the moir\'e patterns for camera acquired screen images; the new technique outperforms the existing ones.