Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display
Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR)...
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MDPI AG
2024-10-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/11/266 |
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| author | Yu Lim Seo Suk-Ju Kang Yeon-Kug Moon |
| author_facet | Yu Lim Seo Suk-Ju Kang Yeon-Kug Moon |
| author_sort | Yu Lim Seo |
| collection | DOAJ |
| description | Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) images, potentially leading to unwanted distortions in the generated SR images. Our paper presents a novel solution by using two-dimensional structure consistency (TSC) for image analysis. The TSC serves as a mask, enabling adaptive analysis based on the unique frequency characteristics of different image regions. Furthermore, a mutual loss mechanism, which dynamically adjusts the training process based on the results filtered by the TSC-based mask, is introduced. Additionally, the TSC loss is proposed to enhance our model capacity to generate precise TSC in high-frequency regions. As a result, our method effectively reduces distortions in high-frequency areas while preserving clarity in regions containing low-frequency components. Our method outperforms other SR techniques, demonstrating superior results in both qualitative and quantitative evaluations. Quantitative measurements, including PSNR, SSIM, and the perceptual metric LPIPS, show comparable PSNR and SSIM values, while the perceptual SR quality is notably improved according to the LPIPS metric. |
| format | Article |
| id | doaj-art-5cfdb1f5974d4639a93d657fbbf0ed94 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-5cfdb1f5974d4639a93d657fbbf0ed942025-08-20T02:04:58ZengMDPI AGJournal of Imaging2313-433X2024-10-01101126610.3390/jimaging10110266Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution DisplayYu Lim Seo0Suk-Ju Kang1Yeon-Kug Moon2Samsung Electronics, Suwon-si 16677, Gyeonggi-do, Republic of KoreaDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of KoreaRecent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) images, potentially leading to unwanted distortions in the generated SR images. Our paper presents a novel solution by using two-dimensional structure consistency (TSC) for image analysis. The TSC serves as a mask, enabling adaptive analysis based on the unique frequency characteristics of different image regions. Furthermore, a mutual loss mechanism, which dynamically adjusts the training process based on the results filtered by the TSC-based mask, is introduced. Additionally, the TSC loss is proposed to enhance our model capacity to generate precise TSC in high-frequency regions. As a result, our method effectively reduces distortions in high-frequency areas while preserving clarity in regions containing low-frequency components. Our method outperforms other SR techniques, demonstrating superior results in both qualitative and quantitative evaluations. Quantitative measurements, including PSNR, SSIM, and the perceptual metric LPIPS, show comparable PSNR and SSIM values, while the perceptual SR quality is notably improved according to the LPIPS metric.https://www.mdpi.com/2313-433X/10/11/266interpolationimage up-scalingsuper resolutiondeep learning |
| spellingShingle | Yu Lim Seo Suk-Ju Kang Yeon-Kug Moon Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display Journal of Imaging interpolation image up-scaling super resolution deep learning |
| title | Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display |
| title_full | Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display |
| title_fullStr | Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display |
| title_full_unstemmed | Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display |
| title_short | Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display |
| title_sort | frequency domain based super resolution using two dimensional structure consistency for ultra high resolution display |
| topic | interpolation image up-scaling super resolution deep learning |
| url | https://www.mdpi.com/2313-433X/10/11/266 |
| work_keys_str_mv | AT yulimseo frequencydomainbasedsuperresolutionusingtwodimensionalstructureconsistencyforultrahighresolutiondisplay AT sukjukang frequencydomainbasedsuperresolutionusingtwodimensionalstructureconsistencyforultrahighresolutiondisplay AT yeonkugmoon frequencydomainbasedsuperresolutionusingtwodimensionalstructureconsistencyforultrahighresolutiondisplay |