Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain

• URL: http://arxiv.org/abs/2504.11286v1
• Date: Tue, 15 Apr 2025 15:26:28 GMT
• Title: Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain
• Authors: Pengcheng Zheng, Kecheng Chen, Jiaxin Huang, Bohao Chen, Ju Liu, Yazhou Ren, Xiaorong Pu,
• Abstract summary: Medical image restoration tasks aim to recover high-quality images from degraded observations, exhibiting emergent desires in many clinical scenarios.<n>Existing deep learning-based restoration methods struggle with rendering computationally-efficient reconstruction results.<n>We present LRformer, a Lightweight Transformer-based method via Reliability-guided learning in the frequency domain.
• Score: 29.81704480466466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image restoration tasks aim to recover high-quality images from degraded observations, exhibiting emergent desires in many clinical scenarios, such as low-dose CT image denoising, MRI super-resolution, and MRI artifact removal. Despite the success achieved by existing deep learning-based restoration methods with sophisticated modules, they struggle with rendering computationally-efficient reconstruction results. Moreover, they usually ignore the reliability of the restoration results, which is much more urgent in medical systems. To alleviate these issues, we present LRformer, a Lightweight Transformer-based method via Reliability-guided learning in the frequency domain. Specifically, inspired by the uncertainty quantification in Bayesian neural networks (BNNs), we develop a Reliable Lesion-Semantic Prior Producer (RLPP). RLPP leverages Monte Carlo (MC) estimators with stochastic sampling operations to generate sufficiently-reliable priors by performing multiple inferences on the foundational medical image segmentation model, MedSAM. Additionally, instead of directly incorporating the priors in the spatial domain, we decompose the cross-attention (CA) mechanism into real symmetric and imaginary anti-symmetric parts via fast Fourier transform (FFT), resulting in the design of the Guided Frequency Cross-Attention (GFCA) solver. By leveraging the conjugated symmetric property of FFT, GFCA reduces the computational complexity of naive CA by nearly half. Extensive experimental results in various tasks demonstrate the superiority of the proposed LRformer in both effectiveness and efficiency.

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