A Green Learning Approach to LDCT Image Restoration
This addresses the need for efficient and transparent image restoration in medical analysis, though it appears incremental as an alternative to existing deep learning methods.
The paper tackles the problem of restoring low-dose computed tomography (LDCT) medical images, which are prone to noise and artifacts, by proposing a green learning approach that achieves state-of-the-art performance with smaller model size and lower inference complexity.
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.