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Image enhancement based on local luminance statistics and nonlinear functions
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Paper Abstract

Image enhancement is a critical processing step for various vision-based applications, and many nonlinear methods are proposed. However, most of them are global techniques, lacking contrast enhancement to bring out fine features and details. Furthermore, they do not work well in various lighting conditions. In order to handle these limitations, an adaptive image enhancement method (AIELN) is proposed based on local luminance statistics and nonlinear functions. The method is composed of three steps: adaptive dynamic range adjustment, adaptive contrast enhancement and color restoration. Dynamic range adjustment is achieved by a series of nonlinear functions with different curvatures designed based on the human vision system, which can adaptively increase the intensity around dark regions and decrease the intensity around bright regions. Contrast enhancement is accomplished by enhancing the intensity of image according to the luminance of the local regions. Finally, the enhanced image is obtained by color restoration in YUV space. Experimental results demonstrate that the proposed method can be effectively improve the quality of the images captured in non-uniform lighting conditions, achieving better balance between dynamic range adjustment and contrast enhancement. Furthermore, the proposed method outperforms several existing methods in terms of quality and efficiency.

Paper Details

Date Published: 18 December 2019
PDF: 8 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113381P (18 December 2019); doi: 10.1117/12.2544453
Show Author Affiliations
Xikui Miao, Key Lab. of Electro-Optical Countermeasures Test and Evaluation Technology (China)
Hengwei Zhang, Key Lab. of Electro-Optical Countermeasures Test and Evaluation Technology (China)
Qili Hu, Key Lab. of Electro-Optical Countermeasures Test and Evaluation Technology (China)
Xiaohu Liu, Key Lab. of Electro-Optical Countermeasures Test and Evaluation Technology (China)


Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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