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Paper Details

Paper Title
Maximum A-Posteriori (MAP) Estimation based Hyperspectral Image Super-Resolution Reconstruction
Abstract
Limited by the existed imagery hardware, it is challenging to obtain a hyperspectral image (HSI) with a high spatial resolution. Super-resolution (SR) focuses on the ways to enhance the spatial resolution. HSI SR is a highly attractive topic in computer vision and has attracted the attention from many researchers. Super-resolution reconstruction (SRR) is a promising signal post-processing technique for hyperspectral image resolution enhancement. This paper proposes a maximum a posteriori (MAP) based multi-frame super-resolution algorithm for hyperspectral images. Principal component analysis (PCA) is utilized in both parts of the proposed algorithm: motion estimation and image reconstruction. A simultaneous motion estimation method with the first few principal components, which contain most of the information of a hyperspectral image, is proposed to reduce computational load and improve motion field accuracy. In the image reconstruction part, different image resolution enhancement techniques are applied to different groups of components, to reduce computational load and simultaneously remove noise.. The experimental results and comparative analyses verify the effectiveness of this algorithm.
KeyWord
Hyperspectral image, Super-resolution, Motion estimation, PCA.
Others Details
Paper Id : 61301
Author Name : R. SUDHEER BABU
Co-Author Name(s) : Dr. K.E.Sreenivasa Murthy
Volume/Issue No : Volume 04 Issue 11
Page No : 178-187
DOI Number : DOI:10.21090/IJAERD.61301
Publication Date : 2017-11-12
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website : http://www.ijaerd.com/index.php
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406