A Matching Pursuit Reconstruction Method Based on Structured Observation and Direction Guidance

Abstract

The invention discloses a matching tracking reconstruction method based on structured observation and direction guidance, which mainly solves the problem of inaccurate and low robustness of compressed sensing image reconstruction under low sampling rate, and the realization process is as follows: according to the sketch of the image , defines sketchable blocks and non-sketchable blocks; clustering based on sketch direction guidance is used for sketchable blocks, and grayscale clustering is used for non-sketchable blocks; when observing, sketch-based representation is used for smooth image block classes in non-sketchable blocks The structured observation of non-smooth image block class adopts multi-measurement vector observation; when reconstructing, the OMP algorithm based on sketch representation and direction guidance is used to obtain the reconstructed image. Compared with the OMP method under traditional observation, the present invention has important advantages With the advantages of high image quality and good robustness, it can be used for the reconstruction of natural images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a matching tracking reconstruction method based on structured observation and direction guidance, which can be used to reconstruct natural images. Background technique [0002] In recent years, a new data theory compressive sensing CS has emerged in the field of signal processing. This theory realizes compression while collecting data, breaks through the limitations of the traditional Nyquist sampling theorem, and brings new advantages to data collection technology. The revolutionary changes make the theory have broad application prospects in compressed imaging systems, military cryptography, wireless sensing and other fields. Compressed sensing theory mainly includes three aspects: sparse representation of signal, observation of signal and reconstruction of signal. [0003] There are three main categories of current CS reconstruction algorithms, including c...

AI Technical Summary

Problems solved by technology

Gaussian observation is a random observation method, which treats all image blocks equally and does not capture the inherent a priori characteristics of image blocks. At the same time, the corresponding OMP reconstruction method directly reconstructs the image according to the measurement, and does not have the image block The judgment of the structure, both of which lead to the lack of accuracy and robustness of the reconstruction at low sampling rates

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