Paper
20 March 2013 Infrared image enhancement based on NSCT and neighborhood information
Author Affiliations +
Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 876833 (2013) https://doi.org/10.1117/12.2010930
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore
Abstract
Infrared images are usually subject to low contrast, edge blurring and a large amount of noise. Aiming at improving the quality of the infrared images, this paper presents a novel adaptive algorithm on infrared image enhancement. Firstly, the input image is decomposed via the nonsubsampled Contourlet transform (NSCT) to achieve the coefficients of subbands at different scales and directions. Next, the coefficients of high frequency are classified into three categories automatically by using an adaptive classification method which analyzes the coefficients in their local neighborhood. After that, a nonlinear mapping function is adopted to modify the coefficients, in order to highlight the edges and suppress the high frequency noise. Finally, the enhanced image is obtained by reconstructing via the above modified coefficients. Experiment results show that the proposed algorithm could effectively enhance image contrast and highlight edges while avoiding the image distortion and noise amplification.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Zhang, Chenxi Zhang, Ding Yuan, and Mingui Sun "Infrared image enhancement based on NSCT and neighborhood information", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876833 (20 March 2013); https://doi.org/10.1117/12.2010930
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image enhancement

Infrared imaging

Infrared radiation

Image processing

Image quality

Distortion

Signal to noise ratio

Back to Top