Paper Details

Limited by the existed imagery hardware, it is challenging to obtain a hyperspectral image (HSI) with a highspatial resolution. Super-resolution (SR) focuses on the ways to enhance the spatial resolution. HSI SR is a highlyattractive topic in computer vision and has attracted the attention from many researchers. Super-resolutionreconstruction (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 hyperspectralimages. Principal component analysis (PCA) is utilized in both parts of the proposed algorithm: motion estimation andimage reconstruction. A simultaneous motion estimation method with the first few principal components, which containmost of the information of a hyperspectral image, is proposed to reduce computational load and improve motion fieldaccuracy. In the image reconstruction part, different image resolution enhancement techniques are applied to differentgroups of components, to reduce computational load and simultaneously remove noise.. The experimental results andcomparative analyses verify the effectiveness of this algorithm.

R.Sudheer Babu, Dr. K.E.Sreenivasa Murthy, “Maximum A-Posteriori (MAP) Estimation based Hyperspectral Image Super-Resolution Reconstruction”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 11, pp. 178-187, November 2017.