Compressed Spectral Imaging Method Based on Nonlinear Compressed Sensing and Dictionary Learning

Abstract

The invention discloses a compressed spectrum imaging method based on nonlinear compressed sensing and dictionary learning, which mainly solves the problem of negative values ​​in the dictionary and sparse coefficients learned in the compressed sampling process in the prior art. The implementation steps are: first project the signal in the original space onto the feature space, introduce non-negative conditions, use the non-negative kernel tracking algorithm and the non-negative matrix decomposition method to perform dictionary learning in the feature space; convert the learned The dictionary is used in the nonlinear compressed sensing model, and the sparse coefficients are obtained through the non-negative kernel tracking algorithm; finally, the pre-image method is used to restore the original signal. Experimental results show that under different sampling rates, compared with other existing dictionary learning methods, the present invention has the best reconstruction effect and can be used for remote sensing image acquisition.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a compressed spectrum imaging method, which can be used for remote sensing image acquisition. Background technique [0002] Compressed sensing is a new sampling theory developed in the field of image processing technology in recent years. By using the sparse characteristics of the signal, it can realize the accurate recovery of information under the condition of much smaller than the traditional Nyquist sampling rate. At present, most of the compressed sensing is done under the linear model, because the sparse representation of the signal under the linear model is simple and intuitive. From the initial orthogonal base dictionary to the current dictionary learning, a large number of related researchers have used various methods to try to find a more suitable description of the transformation space, but they always stay in the linear model, so the development is slow. Ho...

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Problems solved by technology

Although this method can achieve a better reconstruction effect with a lower sampling rate and fewer measurement values, due to the negative value of the learned dictionary, there are information errors and Information loss, the original signal cannot be fully expressed, so that the reconstructed signal has a larger visual error, a smaller PSNR, and a poorer recovery effect than the original signal.

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