Paper Info
Title
Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain
Abstract
To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.
Year
DOI
Venue
2020
10.1007/s11042-019-08586-x
Multimedia Tools and Applications
Keywords
DocType
Volume
Image enhancement, Linear stretch, PLIP, NSST, Adaptive nonlinear enhancement
Journal
79
Issue
ISSN
Citations 
19
1380-7501
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Lanhua Zhang100.34
Zhenhong Jia22915.13
Lucien Koefoed300.68
Jie Yang486887.15
Nikola K Kasabov53645290.73