I3E Technologies, #23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001. IEEE PROJECTS AVAILABLE, NON-IEEE PROJECTS AVAILABLE, APPLICATION BASED PROJECTS AVAILABLE, MINI-PROJECTS AVAILABLE. Mobile : 99436 99916, 99436 99926, 99436 99936 Mail ID: i3eprojectskarur@gmail.com Web: www.i3eprojects.com

1. SPECTRAL–SPATIAL KERNEL REGULARIZED FOR HYPERSPECTRAL IMAGE
DENOISING
ABSTRACT
Noise contamination is a ubiquitous problem in hyperspectral images (HSIs), which is a
challenging and promising theme in many remote sensing applications. A large number of
methods have been proposed to remove noise. Unfortunately, most denoising methods fail to
take full advantages of the high spectral correlation and to simultaneously consider the specific
noise distributions in HSIs. Recently, a spectral–spatial adaptive hyperspectral total variation
(SSAHTV) was proposed and obtained promising results. However, the SSAHTV model is
insensitive to the image details, which makes the edges blur. To overcome all of these
drawbacks, a spectral–spatial kernel method for HSI denoising is proposed in this paper. The
proposed method is inspired by the observation that the spectral–spatial information is highly
redundant in HSIs, which is sufficient to estimate the clear images. In this paper, a spectral–
spatial kernel regularization is proposed to maintain the spectral correlations in spectral
dimension and to match the original structure between two spatial dimensions. Moreover, an
adaptive mechanism is developed to balance the fidelity term according to different noise
distributions in each band. Therefore, it cannot only suppress noise in the high-noise band but
also preserve information in the low-noise band. The reliability of the proposed method in
removing noise is experimentally proved on both simulated data and real data.