Compressed sensing sampling reconstruction method and system based on linear sampling network and generative adversarial residual network

Abstract

The invention discloses a compressed sensing sampling reconstruction method and system based on a linear sampling network and a generative adversarial residual network. The method comprises the steps:obtaining a training image, and segmenting the training image into a plurality of image blocks through segmentation processing; constructing a linear sampling network to measure the image blocks to obtain measurement values corresponding to the image blocks; in the generative adversarial residual network, carrying out linear mapping processing on measurement values of all image blocks through a full connection layer to obtain an initial reconstruction result; inputting the initial reconstruction result into a residual error network, and training to obtain residual error information; performing signal fusion on the initial reconstruction result and the residual error information to obtain a generation result of the generator; jointly inputting a generation result of the generator and the original image block into a discriminator for judgment; and calculating a loss function, and performing iterative training on the linear sampling network and the generative adversarial residual networkto obtain a final image reconstruction result. The method can effectively improve the reconstruction effect at a low sampling rate.

Description

technical field [0001] The invention belongs to the technical field of digital signal processing, and in particular relates to a compression sensing sampling reconstruction method and system based on a linear sampling network and a generated adversarial residual network. Background technique [0002] Compressed sensing (Compressed sensing, CS) is an emerging technology for acquiring and reconstructing digital data, which is currently widely used in the field of image and video. It captures data in the form of compressed sensing measurements and then constructs raw data from these CS measurements. Since the number of measurements required is much smaller than the limit of the Nyquist theory, compressed sensing is an ideal sampling method in many application fields, such as single-pixel cameras and medical scanners. [0003] Traditional compressive sensing reconstruction methods use structured sparse assumptions to model image signals, and use iterative optimization strategie...

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Problems solved by technology

[0009] The present invention aims at the problem that the reconstruction effect of the traditional method is poor at low sampling rates and the reconstruction effect of the deep learning method

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