Dataset Information

A fast blind zero-shot denoiser.

ABSTRACT: Image noise is a common problem in light microscopy. This is particularly true in real-time live-cell imaging applications in which long-term cell viability necessitates low-light conditions. Modern denoisers are typically trained on a representative dataset, sometimes consisting of just unpaired noisy shots. However, when data are acquired in real time to track dynamic cellular processes, it is not always practical nor economical to generate these training sets. Recently, denoisers have emerged that allow us to denoise single images without a training set or knowledge about the underlying noise. But such methods are currently too slow to be integrated into imaging pipelines that require rapid, real-time hardware feedback. Here we present Noise2Fast, which can overcome these limitations. Noise2Fast uses a novel downsampling technique we refer to as 'chequerboard downsampling'. This allows us to train on a discrete 4-image training set, while convergence can be monitored using the original noisy image. We show that Noise2Fast is faster than all similar methods with only a small drop in accuracy compared to the gold standard. We integrate Noise2Fast into real-time multi-modal imaging applications and demonstrate its broad applicability to diverse imaging and analysis pipelines.

SUBMITTER: Lequyer J

PROVIDER: S-EPMC9674521 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A fast blind zero-shot denoiser.

Lequyer Jason J Philip Reuben R Sharma Amit A Hsu Wen-Hsin WH Pelletier Laurence L

Nature machine intelligence 20221031 11

Image noise is a common problem in light microscopy. This is particularly true in real-time live-cell imaging applications in which long-term cell viability necessitates low-light conditions. Modern denoisers are typically trained on a representative dataset, sometimes consisting of just unpaired noisy shots. However, when data are acquired in real time to track dynamic cellular processes, it is not always practical nor economical to generate these training sets. Recently, denoisers have emerged ...[more]

PMID: 36415333

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Project description:Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Dataset Information

A fast blind zero-shot denoiser.

Publications

A fast blind zero-shot denoiser.

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