Moir\'{e} Photo Restoration Using Multiresolution Convolutional Neural Networks

TL;DR

This paper presents a multiresolution convolutional neural network designed to automatically remove moiré patterns from photos of digital screens, significantly improving image quality by analyzing and canceling artifacts across multiple frequency bands.

Contribution

The paper introduces a novel multiresolution CNN architecture specifically for moiré pattern removal and provides a large-scale benchmark dataset for evaluating such algorithms.

Findings

Achieves state-of-the-art performance on the benchmark dataset.

Effectively cancels moiré artifacts across a wide frequency range.

Outperforms existing image restoration methods in moiré removal.

Abstract

Digital cameras and mobile phones enable us to conveniently record precious moments. While digital image quality is constantly being improved, taking high-quality photos of digital screens still remains challenging because the photos are often contaminated with moir\'{e} patterns, a result of the interference between the pixel grids of the camera sensor and the device screen. Moir\'{e} patterns can severely damage the visual quality of photos. However, few studies have aimed to solve this problem. In this paper, we introduce a novel multiresolution fully convolutional network for automatically removing moir\'{e} patterns from photos. Since a moir\'{e} pattern spans over a wide range of frequencies, our proposed network performs a nonlinear multiresolution analysis of the input image before computing how to cancel moir\'{e} artefacts within every frequency band. We also create a large-scale benchmark dataset with $100,000^+$ image pairs for investigating and evaluating moir\'{e} pattern removal algorithms. Our network achieves state-of-the-art performance on this dataset in comparison to existing learning architectures for image restoration problems.