Endoscopic Image Exposure Correction

Citation Author(s):
Xin
Su
Submitted by:
xin su
Last updated:
Mon, 11/04/2024 - 18:06
DOI:
10.21227/f10z-bb73
License:
Creative Commons Attribution
258 Views
Categories:
Artificial Intelligence
Keywords:
Endoscopic Image
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Abstract 

We began by selecting normally exposed images from several prominent endoscopic image repositories, including Kvasir, CVC-ColonDB, CVC-ClinicDB, ETIS-LaribPolypDB, and CVC-300. To simulate exposure anomalies reflective of real-world variations, we utilize the LECARM model, which allows us to apply a random exposure range of (-1,1). This generates a diverse spectrum of exposure anomalies that closely resemble those encountered in clinical settings.

Instructions: 

For the purposes of this study, we provide a subset of the dataset, available at https://dx.doi.org/10.21227/f10z-bb73, to support further research in this domain.

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