KEYWORDS: Photoacoustic imaging, Transformers, Signal to noise ratio, Image quality, Photoacoustic spectroscopy, Image enhancement, Visual process modeling, Performance modeling, Data modeling, Imaging systems
Photoacoustic Imaging faces limitations such as limited acoustic detection and low optical propagation depth, resulting in poor image quality, low signal-to-noise ratio and resultant shallow imaging depth. The research evaluates the performance of various deep learning techniques such as convolutional layers, residual layers, vision transformers when combined with generative adversarial models help in enhancing the quality of photoacoustic images. The evaluation shows promising results for deep learning to improve photoacoustic images to improve the signal-to-noise ratio and enhance the imaging depth in the form of deeper vascular structure in the model outputs.
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