Restoration of Recaptured Screen Images With a Divide and Conquer Strategy

Moiré patterns in recaptured screen images are image defects that can affect image quality to an extreme extent. Different from other image defects, moiré artefacts can vary greatly in scales, colours and shapes. Such moiré patterns mix with image content and disturb image features of different scales in different ways, making moiré pattern removal a challenging task. In this paper, we present a novel divide-and-conquer strategy to solve the problem. In the divide stage, we innovatively decompose images into different layers, as well as into structure components and detail components. Then in the conquer stage, guided by the layers retrieved from the divide stage, we can restore coarse and fine image components independently, which greatly improve the demoiréing performance. Our strategy outperforms state-of-arts in both quantitative and qualitative evaluations.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Big Data
Published
2024-08-13
DOI
https://doi.org/10.1109/tbdata.2024.3442538
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Restoration of Recaptured Screen Images With a Divide and Conquer Strategy

Siu Ming Yiu, Hao Xiong, Yujing Sun
IEEE Transactions on Big Data
Image Processing and 3D Reconstruction
article

Restoration of Recaptured Screen Images With a Divide and Conquer Strategy

Siu Ming Yiu, Hao Xiong, Yujing Sun
article en

Abstract

Moiré patterns in recaptured screen images are image defects that can affect image quality to an extreme extent. Different from other image defects, moiré artefacts can vary greatly in scales, colours and shapes. Such moiré patterns mix with image content and disturb image features of different scales in different ways, making moiré pattern removal a challenging task. In this paper, we present a novel divide-and-conquer strategy to solve the problem. In the divide stage, we innovatively decompose images into different layers, as well as into structure components and detail components. Then in the conquer stage, guided by the layers retrieved from the divide stage, we can restore coarse and fine image components independently, which greatly improve the demoiréing performance. Our strategy outperforms state-of-arts in both quantitative and qualitative evaluations.

IEEE Transactions on Big DataVol. 11(3)
Hong Kong University of Science and Technology (HK), University of Hong Kong (HK)
Openalex Percentile: Top 88%
Image Processing and 3D Reconstruction
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.