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[2602.19540] A Green Learning Approach to LDCT Image Restoration
Summary
This paper presents a Green Learning approach for restoring low-dose computed tomography (LDCT) images, emphasizing mathematical transparency and efficiency while achieving state-of-the-art performance.
Why It Matters
The restoration of LDCT images is crucial for accurate medical analysis. This innovative Green Learning methodology offers a more efficient alternative to traditional deep learning methods, potentially improving diagnostic processes in medical imaging.
Key Takeaways
- Introduces a Green Learning method for LDCT image restoration.
- Demonstrates high performance with lower computational demands.
- Highlights the importance of mathematical transparency in image processing.
- Offers a viable alternative to existing deep learning techniques.
- Potentially enhances the accuracy of medical diagnoses through improved image quality.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.19540 (cs) [Submitted on 23 Feb 2026] Title:A Green Learning Approach to LDCT Image Restoration Authors:Wei Wang, Yixing Wu, C.-C. Jay Kuo View a PDF of the paper titled A Green Learning Approach to LDCT Image Restoration, by Wei Wang and 2 other authors View PDF HTML (experimental) Abstract:This work proposes a green learning (GL) approach to restore medical images. Without loss of generality, we use low-dose computed tomography (LDCT) images as examples. LDCT images are susceptible to noise and artifacts, where the imaging process introduces distortion. LDCT image restoration is an important preprocessing step for further medical analysis. Deep learning (DL) methods have been developed to solve this problem. We examine an alternative solution using the Green Learning (GL) methodology. The new restoration method is characterized by mathematical transparency, computational and memory efficiency, and high performance. Experiments show that our GL method offers state-of-the-art restoration performance at a smaller model size and with lower inference complexity. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19540 [cs.CV] (or arXiv:2602.19540v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2602.19540 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Proceedings of the IEEE Inter...
