denoiSplit: a method for joint image splitting and unsupervised denoising
arxiv(2024)
Abstract
In this work we present denoiSplit, a method to tackle a new analysis task,
i.e. the challenge of joint semantic image splitting and unsupervised
denoising. This dual approach has important applications in fluorescence
microscopy, where semantic image splitting has important applications but noise
does generally hinder the downstream analysis of image content. Image splitting
involves dissecting an image into its distinguishable semantic structures. We
show that the current state-of-the-art method for this task struggles in the
presence of image noise, inadvertently also distributing the noise across the
predicted outputs. The method we present here can deal with image noise by
integrating an unsupervised denoising sub-task. This integration results in
improved semantic image unmixing, even in the presence of notable and realistic
levels of imaging noise. A key innovation in denoiSplit is the use of
specifically formulated noise models and the suitable adjustment of
KL-divergence loss for the high-dimensional hierarchical latent space we are
training. We showcase the performance of denoiSplit across 4 tasks on
real-world microscopy images. Additionally, we perform qualitative and
quantitative evaluations and compare results to existing benchmarks,
demonstrating the effectiveness of using denoiSplit: a single Variational
Splitting Encoder-Decoder (VSE) Network using two suitable noise models to
jointly perform semantic splitting and denoising.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined