FCE-Net: a fast image contrast enhancement method based on deep learning for biomedical optical images
Biomedical Optics Express, ISSN: 2156-7085, Vol: 13, Issue: 6, Page: 3521-3534
2022
- 5Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations5
- Citation Indexes5
- CrossRef3
- Captures8
- Readers8
Article Description
Optical imaging is an important tool for exploring and understanding structures of biological tissues. However, due to the heterogeneity of biological tissues, the intensity distribution of the signal is not uniform and contrast is normally degraded in the raw image. It is difficult to be used for subsequent image analysis and information extraction directly. Here, we propose a fast image contrast enhancement method based on deep learning called Fast Contrast Enhancement Network (FCE-Net). We divided network into dual-path to simultaneously obtain spatial information and large receptive field. And we introduced the spatial attention mechanism to enhance the inter-spatial relationship. We showed that the cell counting task of mouse brain images processed by FCE-Net was with average precision rate of 97.6% ± 1.6%, and average recall rate of 98.4% ± 1.4%. After processing with FCE-Net, the images from vascular extraction (DRIVE) dataset could be segmented with spatial attention U-Net (SA-UNet) to achieve state-of-the-art performance. By comparing FCE-Net with previous methods, we demonstrated that FCE-Net could obtain higher accuracy while maintaining the processing speed. The images with size of 1024 × 1024 pixels could be processed by FCE-Net with 37fps based on our workstation. Our method has great potential for further image analysis and information extraction from large-scale or dynamic biomedical optical images.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85130893757&origin=inward; http://dx.doi.org/10.1364/boe.459347; http://www.ncbi.nlm.nih.gov/pubmed/35781947; https://opg.optica.org/abstract.cfm?URI=boe-13-6-3521; https://dx.doi.org/10.1364/boe.459347; https://opg.optica.org/boe/fulltext.cfm?uri=boe-13-6-3521&id=473057
Optica Publishing Group
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