Share Email Print

Proceedings Paper

Adaptive histogram subsection modification for infrared image enhancement
Author(s): Hui-ming Qu; Qian Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Firstly, the drawbacks of infrared image histogram equalization and its improved algorithm are analyzed. A novel technique which can not only enhance the contrast but also preserve detail information of infrared image is presented. It is called adaptive histogram subsection modification in this paper. The property of infrared image histogram is applied to determine the subsection position adaptively. The second-order differential coefficient of gray level probabilistic density curve is calculated from top down direction. The first inflexion is chosen as the subsection point between high probabilistic density gray levels and low probabilistic density gray levels in the histogram of infrared image. Then the histogram of low probabilistic density section and high probabilistic density section are mapped and modified respectively. Finally, subsection images are combined together and an output infrared image is reconstructed. The contrast is enhanced and the original gray levels are mostly preserved simultaneously during extending the dynamic range of gray levels in infrared image. Meanwhile, suitable distance is kept between gray levels to avoid large isolated grains defined as patchiness in the image. Several infrared images are adopted to demonstrate the performance of this method. Experimental results show that the infrared image quality is greatly improved by this approach. Furthermore, the proposed algorithm is simple and easy to perform.

Paper Details

Date Published: 4 May 2006
PDF: 7 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623310 (4 May 2006); doi: 10.1117/12.660301
Show Author Affiliations
Hui-ming Qu, Nanjing Univ. of Science & Technology (China)
Qian Chen, Nanjing Univ. of Science & Technology (China)

Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top