A Moiré Removal Method Based on Peak Filtering and Image Enhancement

Wenfa Qi, Xinquan Yu, Xiaolong Li and Shuangyong Kang ()
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Wenfa Qi: Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, China
Xinquan Yu: School of Computer Science and Engineering, Ministry of Education Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China
Xiaolong Li: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Shuangyong Kang: Beijing Institute of Information Application Technology, Beijing 100044, China

Mathematics, 2024, vol. 12, issue 6, 1-15

Abstract: Screen photos often suffer from moiré patterns, which significantly affect their visual quality. Although many deep learning-based methods for removing moiré patterns have been proposed, they fail to recover images with complex textures and heavy moiré patterns. Here, we focus on text images with heavy moiré patterns and propose a new demoiré approach, incorporating frequency-domain peak filtering and spatial-domain visual quality enhancement. We find that the content of the text image mainly lies in the central region, whereas the moiré pattern lies in the peak region, in the frequency domain. Based on this observation, a peak-filtering algorithm and a central region recovery strategy are proposed to accurately locate and remove moiré patterns while preserving the text parts. In addition, to further remove the noisy background and paint the missing text parts, an image enhancement algorithm utilising the Otsu method is developed. Extensive experimental results show that the proposed method significantly removes severe moiré patterns from images with better visual quality and lower time cost compared to the state-of-the-art methods.

Keywords: moiré removal; peak filtering; image enhancement; binarisation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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