A coded aperture spectral imaging method based on adaptive dictionary learning

Abstract

The invention discloses a coding aperture spectral imaging method based on self-adaptive dictionary learning, and solves the problem that the reconstruction quality of a spectral image is poor due tothe fact that a redundant dictionary constructed by a traditional method cannot effectively and sparsely represent the target image in the previous coding aperture spectral imaging process. The self-adaptive learning is performed according to the measured value to obtain the redundant dictionary, and the method is used for improving the quality of the reconstructed spectral image. The method comprises the following steps: firstly, transforming an original coded aperture spectral imaging framework, and adopting an overlapping block measurement mode; estimating a plurality of spectral image blocks by using a least square method, constructing a training sample set, and performing adaptive training learning by using the sample set to obtain a new redundant dictionary; substituting the new dictionary into an imaging framework to reconstruct a target spectral image; and finally, carrying out loop iteration on the process until an optimal solution is obtained. The constructed redundant dictionary can adapt to a target image, and the spectral image reconstruction quality in coded aperture spectral imaging is greatly improved.

Description

technical field [0001] The present invention relates to a coded aperture spectral imaging method, in particular to a coded aperture spectral imaging method based on adaptive dictionary learning. The present invention belongs to the field of compressed spectral imaging, which is to apply compressed sensing theory to the spectral imaging process. Background technique [0002] As one of the important branches in the field of optical imaging, spectral imaging technology effectively combines traditional imaging technology and spectral detection technology. This technology obtains the two-dimensional spatial information and one-dimensional spectral information of the target, thus forming a data cube with the combination of maps. The traditional spectral imaging technology can only obtain one section of the data cube in one measurement of the detector, and repeated measurements are required to obtain the target image. The coded aperture spectral imaging technology well applies the...

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Problems solved by technology

In this way, there will be many, such as the selection of training samples and their unknown size. If the selected samples do not match the image to be reconstructed, the trained dictionary cannot effectively sparsely represent the target image, which will affect the spectral imaging effect.

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