Version 1
: Received: 30 November 2023 / Approved: 30 November 2023 / Online: 30 November 2023 (05:47:06 CET)
Version 2
: Received: 30 November 2023 / Approved: 30 November 2023 / Online: 1 December 2023 (03:06:55 CET)
How to cite:
Mou, E.; Wang, H.; Yang, M.; Cao, E.; Chen, Y.; Ran, C.; Pang, Y. Global and Local Enhancement of Low-light Endoscopic Images. Preprints2023, 2023111954. https://doi.org/10.20944/preprints202311.1954.v2
Mou, E.; Wang, H.; Yang, M.; Cao, E.; Chen, Y.; Ran, C.; Pang, Y. Global and Local Enhancement of Low-light Endoscopic Images. Preprints 2023, 2023111954. https://doi.org/10.20944/preprints202311.1954.v2
Mou, E.; Wang, H.; Yang, M.; Cao, E.; Chen, Y.; Ran, C.; Pang, Y. Global and Local Enhancement of Low-light Endoscopic Images. Preprints2023, 2023111954. https://doi.org/10.20944/preprints202311.1954.v2
APA Style
Mou, E., Wang, H., Yang, M., Cao, E., Chen, Y., Ran, C., & Pang, Y. (2023). Global and Local Enhancement of Low-light Endoscopic Images. Preprints. https://doi.org/10.20944/preprints202311.1954.v2
Chicago/Turabian Style
Mou, E., Chunlan Ran and Yu Pang. 2023 "Global and Local Enhancement of Low-light Endoscopic Images" Preprints. https://doi.org/10.20944/preprints202311.1954.v2
Abstract
Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.
Keywords
endoscopic image enhancement; dual attention; higher-order curve function
Subject
Computer Science and Mathematics, Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
