What question did this study set out to answer?

This research aims to enhance low-light images by integrating local textures and global contexts effectively.

July 19, 2026PLoS ONEOpen Access

Enhancing low-light images with MSHCDI-Net: A multi-scale hybrid cross-domain interaction approach

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Randomized trial develops MSHCDI-Net to enhance low-light images, suggesting improved visibility and quality.

This research aims to enhance low-light images by integrating local textures and global contexts effectively.
Developed MSHCDI-Net with hierarchical encoder-decoder architecture for multi-scale extraction.
Implemented a cross-domain interaction mechanism for information exchange between CNN and Transformer.
Conducted experiments on public benchmarks, evaluating metrics such as PSNR and SSIM.
Achieved 23.45 dB PSNR and 0.848 SSIM on LOL-v1.
Recorded 23.74 dB PSNR and 0.910 SSIM on LOL-v2-synthetic.
Obtained 22.24 dB PSNR and 0.868 SSIM on LOL-v2-real, demonstrating competitive performance.

Chen et al. (2026) studied this question.

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July 19, 2026PLoS ONEOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial develops MSHCDI-Net to enhance low-light images, suggesting improved visibility and quality.

This research aims to enhance low-light images by integrating local textures and global contexts effectively.
Developed MSHCDI-Net with hierarchical encoder-decoder architecture for multi-scale extraction.
Implemented a cross-domain interaction mechanism for information exchange between CNN and Transformer.
Conducted experiments on public benchmarks, evaluating metrics such as PSNR and SSIM.
Achieved 23.45 dB PSNR and 0.848 SSIM on LOL-v1.
Recorded 23.74 dB PSNR and 0.910 SSIM on LOL-v2-synthetic.
Obtained 22.24 dB PSNR and 0.868 SSIM on LOL-v2-real, demonstrating competitive performance.

Chen et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark