Image super-resolution based on multifractals in transfer domain
•Most existing methods lack adaptive handling of high-frequency info, causing blur issues.•NSCT generates multiple sub-bands to analyze different frequency information.•A multi-fractals approach better captures image details vs single fractal methods.•NSCT creates stable fractal sets by decomposing...
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Veröffentlicht in: | Signal processing. Image communication 2025-01, p.117221, Article 117221 |
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Sprache: | eng |
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Zusammenfassung: | •Most existing methods lack adaptive handling of high-frequency info, causing blur issues.•NSCT generates multiple sub-bands to analyze different frequency information.•A multi-fractals approach better captures image details vs single fractal methods.•NSCT creates stable fractal sets by decomposing images with unified roughness.•Results show strong performance in both subjective and objective testing.
The goal of image super-resolution technique is to reconstruct high-resolution image with fine texture details from its low-resolution version.On Fourier domain,such fine details are more related to the information in the highfrequency spectrum. Most of existing methods do not have specific modules to handle such high-frequency information adaptively. Thus, they cause edge blur or texture disorder. To tackle the problems, this work explores image super-resolution on multiple sub-bands of the corresponding image, which are generated by NonSubsampled Contourlet Transform (NSCT). Different sub-bands hold the information of different frequency which is then related to the detailedness of information of the given low-resolution image.In this work, such image information detailedness is formulated as image roughness. Moreover, fractals analysis is applied to each sub-band image. Since fractals can mathematically represent the image roughness, it then is able to represent the detailedness (i.e. various frequency of image information). Overall, a multi-fractals formulation is established based on multiple sub-bands image. On each sub-band, different fractals representation is created adaptively. In this way, the image super-resolution process is transformed into a multifractal optimization problem. The experiment result demonstrates the effectiveness of the proposed method in recovering high-frequency details. |
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ISSN: | 0923-5965 |
DOI: | 10.1016/j.image.2024.117221 |