Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement by Muhammad Tariq Siddique, Ibrahim Venkat, Shah Hasan Shah Newaz, Sharul Tajuddin

“…In this paper, we investigate the SSPP face recognition problem in the presence of potential illumination variation and propose a two-fold approach. Firstly, we analyse and quantify the illumination variations in face images and then normalise these variations based on a probabilistic image enhancement approach. …”
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A differential auxiliary semantic interactive network for high-resolution remote sensing image change detection by Yujuan Zhao, Yunliang Hu, Xianwei Han, Lei Zhang, Wei Gao

“…Change detection in high-resolution remote sensing images poses several challenges, including variations in illumination variations and the presence of dense small targets. …”
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Effectiveness of purple led for inactivation of Bacillus subtilis and Escherichia coli bacteria in in vitro sterilizers by A. K. Yaqubi, S. D. Astuti, P.A.D. Permatasari, N. Komariyah, E. Endarko, A. H. Zaidan

“…The first variation is the illumination variation at distances of 3 cm, 6 cm, 9 cm, and 12 cm. …”
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Light adaptive image enhancement for improving visual analysis in intercropping cultivation by Wei Zhong, Wanting Yang, Yunfei Wang, Xiang Dong, Xiaowen Wang, Weidong Jia, Mingxiong Ou, Mingde Yan

“…This study conducted experiments throughout the entire soybean–maize intercropping period to address illumination variation. Based on the height difference between crops, solar elevation angle, and light intensity at the top of the soybean canopy, an illumination compensation regression model was developed. …”
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Structure-guided deep learning for back acupoint localization via bone-measuring constraints by Yulong Wang, Tian Lan, Wenjian Dou, Zhi Chen, Song Zhang, Gong Chen, Gong Chen

“…On the obese subset, the NME decreased from 1.5% to 0.8%, FR@1 cm dropped from 4.0% to 1.3%, and precision improved from 83.8% to 93.4%. Under illumination variation, the model achieved an NME of 0.9%, outperforming both HRFormer (1.3%) and HRFormer+SG-KEM (1.1%), with corresponding increases in AUC and precision. …”
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