A self-adaptive remote sensing image enhancement method based on gradient and intensity histogram

Zhuanli Lu; Jiahang Liu; Tieqiao Chen; Chaomeng Kang; Kai Yu

doi:10.1117/12.2285168

24 October 2017 A self-adaptive remote sensing image enhancement method based on gradient and intensity histogram

Zhuanli Lu, Jiahang Liu, Tieqiao Chen, Chaomeng Kang, Kai Yu

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104623I (2017) https://doi.org/10.1117/12.2285168
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

It is crucial to enhance the lower contrast Remote sensing images to obtain more details information for further remote sensing image processing and application. In this letter here, a self-adaptive remote sensing image contrast enhancement method has been proposed. The method is an improvement, based on gradient and intensity histogram equalization (GIHE) by using the advantage of histogram compaction transform (HCT). Firstly, we obtained two enhanced images by GIHE and HCT, respectively. Then furthermore, the two enhanced images were normalized with a self-adaptive paremeter, which based on standard deviation and mean of the gradient. Finally and then, we modified the normalized image by dual-gamma function for preserving the local details. It’s evidenced that the proposed method have more richer details and better subjective visual quality, compared with the other methods. The experimental results depicted in terms of PSNR, MAE and Q. Comparing with the other methods, the proposed method had richer details and better subjective visual quality.

Citation Download Citation

Zhuanli Lu, Jiahang Liu, Tieqiao Chen, Chaomeng Kang, and Kai Yu "A self-adaptive remote sensing image enhancement method based on gradient and intensity histogram", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104623I (24 October 2017); https://doi.org/10.1117/12.2285168

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available