  Photoacoustic imaging is a new imaging technology in recent years, which
combines the advantages of high resolution and rich contrast of optical imaging
with the advantages of high penetration depth of acoustic imaging.
Photoacoustic imaging has been widely used in biomedical fields, such as brain
imaging, tumor detection and so on. The signal-to-noise ratio (SNR) of image
signals in photoacoustic imaging is generally low due to the limitation of
laser pulse energy, electromagnetic interference in the external environment
and system noise. In order to solve the problem of low SNR of photoacoustic
images, we use feedforward denoising convolutional neural network to further
process the obtained images, so as to obtain higher SNR images and improve
image quality. We use Python language to manage the referenced Python external
library through Anaconda, and build a feedforward noise-reducing convolutional
neural network on Pycharm platform.We first processed and segmated a training
set containing 400 images, and then used it for network training. Finally, we
tested it with a series of cerebrovascular photoacoustic microscopy images.The
results show that the peak signal-to-noise ratio (PSNR) of the image increases
significantly before and after denoising.The experimental results verify that
the feed-forward noise reduction convolutional neural network can effectively
improve the quality of photoacoustic microscopic images, which provides a good
foundation for the subsequent biomedical research.
