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Deep Learning-Based Super-Resolution for Vessel Enhancement in Photoacoustic Microscopy Imaging
Thi Thu Ha Vu1, Soonhyuk Tak1, Tan Hung Vo2
1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan, Republic of Korea.
Journal of Biophotonics
|March 4, 2026
Summary
Researchers developed GDSU-Net, a deep learning model, to enhance photoacoustic imaging (PAI) resolution. This novel approach improves vascular network visualization by effectively reconstructing super-resolution (SR) PAI data.
Area of Science:
- Biomedical Imaging
- Artificial Intelligence
- Medical Technology
Background:
- Photoacoustic imaging (PAI) offers high-resolution vascular visualization but faces limitations in spatial resolution, depth, and image quality due to noise and artifacts.
- Existing PAI techniques struggle with the trade-off between resolution and imaging depth, impacting diagnostic capabilities.
Purpose of the Study:
- To introduce GDSU-Net, a novel neural network for super-resolution (SR) reconstruction in PAI.
- To address the performance constraints of PAI by enhancing image resolution and quality.
Main Methods:
- GDSU-Net, based on the U-Net architecture, integrates group normalization, depthwise separable convolutions, SE blocks, and a pixelshuffle-based decoder.
- The model was fine-tuned for SR reconstruction of PAI data.
Main Results:
- GDSU-Net achieved a structural similarity index of 0.889 and a PSNR of 31.979 dB.
- The model significantly reduced RMSE to 0.032 and MAE to 0.025.
- Visual evaluations confirmed effective restoration of vascular details with anatomical fidelity.
Conclusions:
- GDSU-Net demonstrates superior performance in SR reconstruction for PAI.
- The developed model offers a computationally efficient solution for enhancing PAI resolution and image quality.
- GDSU-Net has the potential to improve diagnostic accuracy in applications relying on vascular imaging.
