Fluorescence microscopy images denoising via deep convolutional sparse coding

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 den...

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Vydáno v:	Signal processing. Image communication Ročník 117; s. 117003
Hlavní autoři:	Chen, Ge, Wang, Jianjun, Wang, Hailin, Wen, Jinming, Gao, Yi, Xu, Yongjian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.09.2023
Témata:	Deep convolutional neural network Deep learning Dilated convolution Fluorescence microscopy images denoising Multi-layer convolutional sparse coding Fluorescence microscopy images denoising Deep learning Dilated convolution Deep convolutional neural network Multi-layer convolutional sparse coding
ISSN:	0923-5965, 1879-2677
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Popis
Shrnutí:	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. •The network is an extension of pursuit algorithm (ML-LISTA).•The network can be deepened without introducing additional parameters.•The introduction of dilated convolution improves the denoising effect.•Our method achieves attractive results in all the comparison methods.
ISSN:	0923-5965 1879-2677
DOI:	10.1016/j.image.2023.117003