LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising
Dayang Wang Yongshun Xu Shuo Han Zhan Wu Li Zhou Bahareh Morovati Hengyong Yu
Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure
but at the cost of compromised image quality, characterized by increased noise
and artifacts. Recently, transformer models emerged as a promising avenue to
enhance LDCT image quality. However, the success of such models relies on a
large amount of paired noisy and clean images, which are often scarce in
clinical settings. In the fields of computer vision and natural language
processing, masked autoencoders (MAE) have been recognized as an effective
label-free self-pretraining method for transformers, due to their exceptional
feature representation ability. However, the original pretraining and
fine-tuning design fails to work in low-level vision tasks like denoising. In
response to this challenge, we redesign the classical encoder-decoder learning
model and facilitate a simple yet effective low-level vision MAE, referred to
as LoMAE, tailored to address the LDCT denoising problem. Moreover, we
introduce an MAE-GradCAM method to shed light on the latent learning mechanisms
of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and
generability across a variety of noise levels. Experiments results show that
the proposed LoMAE can enhance the transformer's denoising performance and
greatly relieve the dependence on the ground truth clean data. It also
demonstrates remarkable robustness and generalizability over a spectrum of
noise levels.