Non-linear compression spectral imaging method based on hidden space worked example learning

Abstract

The invention discloses a non-linear compression spectral imaging method based on hidden space worked example learning, and mainly solves the problems of over high time complexity and space complexity in dictionary learning under a non-linear space by using a kernel function in the prior art. The implementing steps include a first step of pre-processing a training sample, and obtaining a virtual training sample; a second step of training the virtual training sample via a linear dictionary learning method, and obtaining a sparse dictionary; a third step of initializing an observation matrix randomly, and achieving non-linear compression sensing spectral imaging through a kernel compression sensing method; and a fourth step of recovering an original signal by using a pre-image method. The experimental result shows that the method of the invention has better reconstruction results and much lower time complexity at the same sampling rate as compared with a conventional KPCA dictionary learning method, and can be used for low rate sampling and recovery of hyperspectral images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a compressed spectrum imaging method, which can be used for low-cost acquisition of hyperspectral images. Background technique [0002] Compressed sensing is a new sampling theory developed in the field of signal processing technology in recent years. By using the sparse and compressible characteristics of the signal, it can realize the accurate recovery of information under the condition of much smaller than the traditional Nyquist sampling rate. Existing compressed sensing methods are all based on explicit linear sparse representation models. The linear sparse representation model has the advantages of being simple, intuitive, easy to understand, and easy to operate. However, the actual scene information is more complicated, and it is difficult to obtain a sufficiently sparse representation under the linear sparse representation model. If the linear spars...

AI Technical Summary

Problems solved by technology

However, the size of the kernel matrix constructed after the introduction of the kernel function depends on the number of samples, which is often relatively large and needs to be kept during the operation process. Therefore, dictionary learning based on 'kernelization' has a high time complexity. and space complexity

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