A Dual-Branch Multidomain Feature Fusion Network for Axial Super-Resolution in Optical Coherence Tomography

• Published in: IEEE signal processing letters Vol. 32; pp. 461 - 465
• Main Authors: Xu, Quanqing, He, Xiang, Xu, Muhao, Hu, Kaixuan, Song, Weiye
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Axial super-resolution; Bandwidth; Convolution; Data mining; Diagnosis; Feature extraction; Frequency-domain analysis; Human performance; Image acquisition; Image reconstruction; Image resolution; Medical imaging; Modules; multidomain feature fusion; Optical Coherence Tomography; Retina; Signal resolution; Spatial resolution; Superresolution; Tomography
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2024.3509337

High-resolution retinal optical coherence tomography(OCT) images are crucial for the diagnosis of numerous retinal diseases, but images acquired by narrow bandwidth OCT devices suffer from axial resolution degradation and are difficult to support disease diagnosis. Deep learning-based methods can enhance the axial resolution of OCT images, but most methods focus on improving the model architecture, the potential of fully exploiting the fusion of spatial and frequency domain information for image reconstruction has not been fully explored. This paper proposes a Dual-branch Multidomain Feature Fusion Network (MDFNet). The core module of the model consists of a parallel Enhanced Multi-scale Spatial Feature module and an Auxiliary Frequcy Feature module to provide non-interfering dual-domain feature information to improve the reconstruction effect. MDFNet achieved the best performance in the tests of mouse retina and human retina datasets, outperforming the state-of-the-art (SOTA) algorithms by 0.11 dB and 0.18 dB respectively. In addition, the results of this method performed best in the retinal layer segmentation test.