Hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity

Abstract

The invention discloses a hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity, and mainly solves the problems in the prior art that reconstruction accuracy is low and the effect is poor after compressed sampling of hyper-spectral data. The hyper-spectral compression perception reconstruction method comprises the steps that 1. the hyper-spectral data are inputted and vectorized; 2. the vectorized hyper-spectral data are sampled so that sampling data are obtained; 3. initial reconstruction of the sampling data is performed; 4. the initially reconstructed data are clustered; 5. the sampling data are classified according to the type of image elements so that various types of sampling data are obtained; 6. a secondary reconstruction model is constructed; and 7. The secondary reconstruction model is solved according to various types of sampling data so that the optimal data of secondary reconstruction are obtained, and the data act as the final reconstruction data. The idea of nonlocal total variation and clustering is introduced on the basis of low-rank sparse reconstruction so that the hyper-spectral compression perception reconstruction method has advantages of high reconstruction accuracy and great effect and can be used for hyper-spectral data imaging.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to a hyperspectral data compression sensing reconstruction method, which can be used for hyperspectral imaging. Background technique [0002] Hyperspectral data is composed of hundreds of very narrow bands. The high-resolution characteristics of its space and band direction make hyperspectral data have a large data dimension. The huge amount of information has a huge impact on the storage, transmission and subsequent processing of hyperspectral data. Here comes the difficulty. The traditional compression sampling method uses the Nyquist sampling rate to uniformly or non-uniformly sample the signal, and then compresses it through algorithms such as prediction, transformation, and vector quantization. This high-redundancy sampling and recompression process causes a great waste of resources, which puts great pressure on airborne or spaceborne applications with low power ...

AI Technical Summary

Problems solved by technology

Although this method can transfer the PCA dimension reduction step from the encoding end to the decoding end, and the reconstruction time is shorter, the quality of the reconstructed image is low, which is not enough to provide a reliable data source for subsequent image processing

Images