用绿色学习法修复低剂量CT图像，模型小速度快效果好
A Green Learning Approach to LDCT Image Restoration
- 采用绿色学习方法，数学透明且计算高效
- 模型更小、推理复杂度更低，性能达顶尖水平
- 适合医疗图像处理中对效率与精度双重要求的场景
本文提出一种绿色学习（Green Learning, GL）方法用于医学图像恢复，以低剂量计算机断层扫描（LDCT）图像为例。LDCT图像易受噪声和伪影影响，成像过程引入失真，其恢复是后续医学分析的重要预处理步骤。深度学习方法已被广泛研究解决该问题。本文探索了一种基于绿色学习的新解决方案，具有数学透明性、计算与内存效率高及高性能的特点。实验表明，所提GL方法在更小的模型规模和更低的推理复杂度下，实现了当前最优的恢复性能。
原文摘要 · Abstract (English)
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.
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