Multiscale Coarse-to-Fine Guided Screenshot Demoireing

In this letter, we propose a multiscale coarse-to-fine guided screenshot demoireing algorithm. We first extract the multiscale features of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moire patterns with the guidance of multiscale information by exp...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 30; pp. 1 - 5
Main Authors Nguyen, Duong Hai, Lee, Se-Ho, Lee, Chul
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Convolution
convolutional neural networks (CNNs)
Correlation
Feature extraction
Image demoireing
Image restoration
Interpolation
Moire patterns
Performance enhancement
Signal processing algorithms
Training
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Summary:In this letter, we propose a multiscale coarse-to-fine guided screenshot demoireing algorithm. We first extract the multiscale features of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moire patterns with the guidance of multiscale information by exploiting the correlation between moire frequencies. To this end, we design two blocks for feature modulation and moire pattern removal. In addition, to further improve the performance, we develop an adaptive reconstruction loss to direct the network to focus on regions that are difficult to restore. Experimental results on multiple datasets demonstrate that the proposed algorithm provides comparable or even better demoireing performance than state-of-the-art algorithms.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2023.3296039