Fluorescence microscopy images denoising via deep convolutional sparse coding

Abstract

Fluorescence microscopy images captured in low light and short exposure time conditions are always contaminated by photons and readout noises, which reduce the fluorescence microscopy images quality. In most cases, this kind of noise can be modeled as Poisson–Gaussian noise. Correspondingly, its denoising task has always been a hot but challenging topic in recent years. In this paper, by integrating model-driven and learning-driven methodologies, we propose an end-to-end supervised neural network for fluorescence microscopy images denoising, named MCSC-net, which embeds the multi-layer learned iterative soft threshold algorithm (ML-LISTA) into deep convolutional neural network (DCNN). Our approach not only uses the strong learning ability of DCNN to adaptively update all parameters in the ML-LISTA, but also introduces dilated convolution into network training without additional parameters to improve denoising performance. In addition, compared with several related methods on a real data set of fluorescence microscopy images, MCSC-net achieves the best denoising effects both in qualitative and quantitative aspects, which shows its strong appeal in practical denoising applications.