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Updated: May 22, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
Published on: July 11, 2025
Content-aware dark image enhancement through channel division
Adin Ramirez Rivera1, Byungyong Ryu, Oksam Chae
1Department of Computer Engineering, Kyung Hee University, Gyeonggido, South Korea. adin@khu.ac.kr
Abstract:
The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images-e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images-without introducing artifacts, which is an improvement over many existing methods.
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