SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912534182756352 |
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| author | Abolade, Ilerioluwakiiye Idoko, Emmanuel Odelola, Solomon Omoigui, Promise Adebanwo, Adetola Iorumbur, Aondana Anazodo, Udunna Crimi, Alessandro Confidence, Raymond |
| author_facet | Abolade, Ilerioluwakiiye Idoko, Emmanuel Odelola, Solomon Omoigui, Promise Adebanwo, Adetola Iorumbur, Aondana Anazodo, Udunna Crimi, Alessandro Confidence, Raymond |
| contents | Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols reduce radiation exposure in children but introduce substantial noise that can obscure critical anatomical details. Conventional denoising methods often degrade fine details, compromising diagnostic accuracy. In this paper, we present SharpXR, a structure-aware dual-decoder U-Net designed to denoise low-dose pediatric X-rays while preserving diagnostically relevant features. SharpXR combines a Laplacian-guided edge-preserving decoder with a learnable fusion module that adaptively balances noise suppression and structural detail retention. To address the scarcity of paired training data, we simulate realistic Poisson-Gaussian noise on the Pediatric Pneumonia Chest X-ray dataset. SharpXR outperforms state-of-the-art baselines across all evaluation metrics while maintaining computational efficiency suitable for resource-constrained settings. SharpXR-denoised images improved downstream pneumonia classification accuracy from 88.8% to 92.5%, underscoring its diagnostic value in low-resource pediatric care. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_08518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays Abolade, Ilerioluwakiiye Idoko, Emmanuel Odelola, Solomon Omoigui, Promise Adebanwo, Adetola Iorumbur, Aondana Anazodo, Udunna Crimi, Alessandro Confidence, Raymond Image and Video Processing Computer Vision and Pattern Recognition Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols reduce radiation exposure in children but introduce substantial noise that can obscure critical anatomical details. Conventional denoising methods often degrade fine details, compromising diagnostic accuracy. In this paper, we present SharpXR, a structure-aware dual-decoder U-Net designed to denoise low-dose pediatric X-rays while preserving diagnostically relevant features. SharpXR combines a Laplacian-guided edge-preserving decoder with a learnable fusion module that adaptively balances noise suppression and structural detail retention. To address the scarcity of paired training data, we simulate realistic Poisson-Gaussian noise on the Pediatric Pneumonia Chest X-ray dataset. SharpXR outperforms state-of-the-art baselines across all evaluation metrics while maintaining computational efficiency suitable for resource-constrained settings. SharpXR-denoised images improved downstream pneumonia classification accuracy from 88.8% to 92.5%, underscoring its diagnostic value in low-resource pediatric care. |
| title | SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.08518 |