Spectral–spatial kernel regularized for hyperspectral image denoising

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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.

This document proposes a method for simultaneously visualizing and segmenting hyperspectral data. The method treats the hyperspectral data set as a single image created by weighted combination of spectral points for each pixel, with the weights optimized to improve human visibility. A piecewise-constant smoothness constraint on the weights prevents blurring in the fused image. Optimizing the weight matrix also provides information to spatially segment the original hyperspectral data set. Experimental results demonstrate the effectiveness of the proposed method.

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This document summarizes a paper on hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation. The paper develops a sparse representation based noise reduction method for hyperspectral imagery that introduces nonlocal similarity and spectral-spatial structure. It partitions images into groups of similar patches that are sparsely represented using shared dictionary atoms, distinguishing true signals from noise. It also uses 3D blocks instead of 2D patches for sparse coding to exploit spectral and spatial correlations. A mixed Poisson and Gaussian noise model is used to handle different noise types in hyperspectral imagery. A variance-stabilizing transformation makes sparse representation insensitive to varying noise distributions across blocks. The proposed method is validated on synthetic and real hyperspectral data sets.

This document summarizes a paper on hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation. The paper develops a sparse representation based noise reduction method for hyperspectral imagery that introduces nonlocal similarity and spectral-spatial structure. It partitions images into groups of similar patches that are sparsely represented using shared dictionary atoms, distinguishing true signals from noise. It also uses 3D blocks instead of 2D patches for sparse coding to exploit spectral and spatial correlations. A mixed Poisson and Gaussian noise model is used to handle different noise types in hyperspectral imagery. A variance-stabilizing transformation makes sparse representation insensitive to varying noise distributions across blocks. The method is validated on synthetic and real hyperspectral data sets.

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