  As the rapid growth of high-speed and deep-tissue imaging in biomedical
research, it is urgent to find a robust and effective denoising method to
retain morphological features for further texture analysis and segmentation.
Conventional denoising filters and models can easily suppress perturbative
noises in high contrast images. However, for low photon budget multi-photon
images, high detector gain will not only boost signals, but also bring huge
background noises. In such stochastic resonance regime of imaging,
sub-threshold signals may be detectable with the help of noises. Therefore, a
denoising filter that can smartly remove noises without sacrificing the
important cellular features such as cell boundaries is highly desired. In this
paper, we propose a convolutional neural network based autoencoder method,
Fully Convolutional Deep Denoising Autoencoder (DDAE), to improve the quality
of Three-Photon Fluorescence (3PF) and Third Harmonic Generation (THG)
microscopy images. The average of the acquired 200 images of a given location
served as the low-noise answer for DDAE training. Compared with other widely
used denoising methods, our DDAE model shows better signal-to-noise ratio (26.6
and 29.9 for 3PF and THG, respectively), structure similarity (0.86 and 0.87
for 3PF and THG, respectively), and preservation of nuclear or cellular
boundaries.
