MRFormer: Multiscale retractable transformer for medical image progressive denoising via noise level estimation

Abstract

Clear medical images are important for auxiliary diagnoses, but the images generated by various medical devices inevitably contain considerable noise. Although various models have been proposed for denoising, these methods ignore the fact that different types of medical images have different noise levels, which leads to unsatisfactory test results. In addition, collecting many medical images for training denoising models consumes many material resources. To address these issues, we formulate a progressive denoising architecture that contains preliminary and profound denoising. First, we construct a noise level estimation network to estimate the noise level via self-supervised learning and perform preliminary denoising with a dilated blind-spot network. Second, with the learned noise distribution, we synthesize noisy natural images to construct clean-noisy natural image pairs. Finally, we design a novel medical image denoising model for profound denoising by training these pairs. The proposed three-stage learning scheme and progressive denoising architecture not only solve the problem that the denoising model only adapts to a single noise level but also alleviate the lack of medical image pairs. Moreover, we integrate dense attention and sparse attention to constitute the retractable transformer module in the profound denoising model, which reconciles a wider receptive field and enhances the representation ability of the transformer, s allowing the denoising model to obtain retractable attention on the input feature and capture more local and global receptive fields simultaneously. The results of qualitative and quantitative experiments demonstrate the effectiveness of our method in removing noise at various levels.