Share Email Print
cover

Proceedings Paper

Research on HDR image fusion algorithm based on Laplace pyramid weight transform with extreme low-light CMOS
Author(s): Wen Guan; Li Li; Weiqi Jin; Su Qiu; Yan Zou
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

Extreme-Low-Light CMOS has been widely applied in the field of night-vision as a new type of solid image sensor. But if the illumination in the scene has drastic changes or the illumination is too strong, Extreme-Low-Light CMOS can’t both clearly present the high-light scene and low-light region. According to the partial saturation problem in the field of night-vision, a HDR image fusion algorithm based on the Laplace Pyramid was researched. The overall gray value and the contrast of the low light image is very low. We choose the fusion strategy based on regional average gradient for the top layer of the long exposure image and short exposure image, which has rich brightness and textural features. The remained layers which represent the edge feature information of the target are based on the fusion strategy based on regional energy. In the process of source image reconstruction with Laplacian pyramid image, we compare the fusion results with four kinds of basal images. The algorithm is tested using Matlab and compared with the different fusion strategies. We use information entropy, average gradient and standard deviation these three objective evaluation parameters for the further analysis of the fusion result. Different low illumination environment experiments show that the algorithm in this paper can rapidly get wide dynamic range while keeping high entropy. Through the verification of this algorithm features, there is a further application prospect of the optimized algorithm. Keywords: high dynamic range imaging, image fusion, multi-exposure image, weight coefficient, information fusion, Laplacian pyramid transform.

Paper Details

Date Published: 8 October 2015
PDF: 10 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967524 (8 October 2015); doi: 10.1117/12.2199818
Show Author Affiliations
Wen Guan, Beijing Institute of Technology (China)
Li Li, Beijing Institute of Technology (China)
Weiqi Jin, Beijing Institute of Technology (China)
Su Qiu, Beijing Institute of Technology (China)
Yan Zou, Nanjing Military Representation Bureau (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

© SPIE. Terms of Use
Back to Top