AIM 2019 Challenge on Image Demoireing: Dataset and Study

TL;DR

This paper presents LCDMoire, a new dataset for image demoireing, and reports on a challenge that benchmarks current methods, advancing research in removing moire patterns from images.

Contribution

Introduces the first large-scale dataset for image demoireing and provides a comprehensive study through a challenge to evaluate state-of-the-art methods.

Findings

Benchmark results of current demoireing methods

Identification of strengths and weaknesses of existing approaches

Establishment of a new standard for future research

Abstract

This paper introduces a novel dataset, called LCDMoire, which was created for the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. The dataset comprises 10,200 synthetically generated image pairs (consisting of an image degraded by moire and a clean ground truth image). In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.