Impact of different loss functions on denoising of microscopic images

The denoising of super-resolution structured illumination microscopy (SR-SIM) images using deep convolutional neural networks (CNNs) has re-ceived much attention in recent years. The contrast and fidelity of biological cell structures in denoised SR-SIM images critically depend on the CNN architecture and the chosen loss function. In this work, we propose two new combinations of loss functions for denoising SR-SIM images with low signal-to-noise ratio. The first combination consists of a loss component computed in the frequency domain by applying Fast Fourier Transform (FFT) to the image data and of pixel-wise loss. The second combination combines the loss in the frequency domain with a feature-based loss. In a series of experiments, these new loss functions are compared with various traditional loss functions for image de-noising. The results show that CNNs trained with the novel losses are superior in terms of PSNR and SSIM values. Moreover, critical structures of biological cells are better preserved in the denoised network output. Further results on the BSD dataset are also promising.