A hyperspectral image inpainting method based on E-3DTV regularity

Abstract

A hyperspectral image inpainting method based on E-3DTV regularity is provided. The method comprises the steps of expanding the original three-dimensional hyperspectral data with noise into matrix along the spectral dimension, and initializing the matrix representation of the noise term and the hyperspectral data to be repaired, and other model variables and parameters under the ADMM framework; performing differential operation on the hyperspectral data to be repaired along horizontal, vertical and spectral dimensions to obtain three gradient maps in different directions, which are expanded into matrices along the spectral dimensions; decomposing the gradient graph matrices in three directions by low rank UV, and constraining the basis matrices of the gradient graph by sparsity to obtain E-3DTV regularity; adding the E-3DTV regularity to the data to be repaired, writing out the optimization model, using the ADMM framework to solve iteratively; obtaining the restoration image and noisewhen the iteration is stable. The invention performs denoising and compression reconstruction on the hyperspectral image data, and improves the enhancement of the traditional 3DTV so as to give consideration to the structural correlation and sparsity of the gradient image, thereby overcoming the defect that the traditional 3DTV can only depict the sparsity of the gradient image and ignores the correlation.

Description

technical field [0001] The present invention relates to an enhanced improvement to the traditional 3DTV, so that the new regularization can take into account the correlation and sparsity of the gradient image of the hyperspectral image, which is different from the traditional 3DTV which can only describe the sparsity of the gradient image and ignore the its essential relevance. Specifically, it relates to denoising and compression reconstruction methods for hyperspectral image data. Background technique [0002] The spectral imager carried on the satellite can form images under many continuous spectral segments of the same ground feature at the same time, so each pixel in the spectral dimension will form a unique spectral curve, which is a distinctive feature to distinguish different substances. Therefore, compared with traditional images, hyperspectral images have richer information in the spectral dimension, so they are widely used in ground object recognition, urban plan...

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Problems solved by technology

For example, the method based on the similarity of the hyperspectral image ignores the local smoothness of the hyperspectral image, and the local smoothness based on the image cannot describe the similarity of the spectrum well, although there are some priors based on two or even three types Fusion methods, but the prior knowledge of these methods usually uses simple superposition, which cannot fully describe the rich prior structural coupling information of the hyperspectral map

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