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.

Dataset Files

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[1] Xin Su, "Endoscopic Image Exposure Correction", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/f10z-bb73. Accessed: Jan. 19, 2025.
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doi = {10.21227/f10z-bb73},
url = {http://dx.doi.org/10.21227/f10z-bb73},
author = {Xin Su },
publisher = {IEEE Dataport},
title = {Endoscopic Image Exposure Correction},
year = {2024} }
TY - DATA
T1 - Endoscopic Image Exposure Correction
AU - Xin Su
PY - 2024
PB - IEEE Dataport
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Xin Su. (2024). Endoscopic Image Exposure Correction. IEEE Dataport. http://dx.doi.org/10.21227/f10z-bb73
Xin Su, 2024. Endoscopic Image Exposure Correction. Available at: http://dx.doi.org/10.21227/f10z-bb73.
Xin Su. (2024). "Endoscopic Image Exposure Correction." Web.
1. Xin Su. Endoscopic Image Exposure Correction [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/f10z-bb73
Xin Su. "Endoscopic Image Exposure Correction." doi: 10.21227/f10z-bb73