A compressed sensing image reconstruction method based on zero-padding operation

Abstract

The invention discloses a compression sensing image reconstruction method based on a zero-padding operation, comprising the following steps: firstly, a total observation number M is set according to the image resolution of a target image to be detected; Secondly, according to the total observation times M, the number of 1 in the measurement matrix is set, and M measurement matrices are generated through the algorithm program. Then, the control signal is sequentially generated into M measurement matrices by using the digital micromirror device, the beam is transmitted or refracted to the targetimage area through the digital micromirror device, and the observation signals corresponding to each measurement matrix are sequentially received by the receiver to obtain M measurement values, and the measurement results are composed; the control signal is transmitted or refracted to the target image area through the digital micromirror device. Thirdly, according to the measurement results and measurement matrix, the target image is reconstructed by the reconstruction algorithm. At last, that target image to be detect is zeroed, and the above step are repeated to obtain a new reconstructed image. The invention improves the resolution of the image to be processed through the zero-padding operation, thereby effectively suppressing the fence effect when sampling, and improving the precisionof the reconstructed image.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a compression sensing image reconstruction method based on zero padding operation. Background technique [0002] Compressed sensing theory is one of the great breakthroughs in the field of signal processing in recent years. It makes full use of the global characteristics of the data, and breaks through the limitation of the Nyquist sampling theorem to a certain extent by reducing the dimensionality of the target signal, thereby reducing the requirements for data acquisition hardware and providing a better solution for signal acquisition, transmission and storage. Compared with traditional image processing technology, compressed sensing technology also has many advantages such as noise reduction and anti-interference. [0003] In the process of image processing, the resolution and precision of the image are the goals that are constantly pursued. However, under the framework of t...

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Problems solved by technology

However, under the framework of the existing compressed sensing technology, due to the characteristics of the reconstruction algorithm and the diversity of the measurement matrix, as well as the error and instability of the hardware in the actual system, the accuracy and resolution of the reconstructed image are often Unable to achieve the expected effect, which limits the advantages of compressed sensing technology to a certain extent

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