DOAJ Open Access 2025

Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network

Wende Dong Yanli Zhang Luqi Hu Songde Liu Chao Tian

Abstrak

Ring-Array photoacoustic tomography (PAT) systems have shown great promise in non-invasive biomedical imaging. However, images produced by these systems often suffer from quality degradation due to non-ideal imaging conditions, with common issues including blurring and streak artifacts. To address these challenges, we propose an image restoration method based on a conditional generative adversarial network (CGAN) framework. Our approach integrates a hybrid spatial and channel attention mechanism within a Residual Shifted Window Transformer Module (RSTM) to enhance the generator’s performance. Additionally, we have developed a comprehensive loss function to balance pixel-level accuracy, detail preservation, and perceptual quality. We further incorporate a gamma correction module to enhance the contrast of the network’s output. Experimental results on both simulated and in vivo data demonstrate that our method significantly improves resolution and restores overall image quality.

Topik & Kata Kunci

Physics Acoustics. Sound Optics. Light

Penulis (5)

Wende Dong

Yanli Zhang

Luqi Hu

Songde Liu

Chao Tian

Format Sitasi

APA MLA BibTeX

Dong, W., Zhang, Y., Hu, L., Liu, S., Tian, C. (2025). Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network. https://doi.org/10.1016/j.pacs.2025.100714

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.1016/j.pacs.2025.100714
Akses: Open Access ✓