Method and system for l1-norm minimization sparse reconstruction with weight reset at low sampling rate
A sparse reconstruction, low sampling rate technology, applied in the field of information processing, can solve problems such as insufficient accuracy, insufficient speed, and large number of projections, and achieve the effect of high reconstruction accuracy
Active Publication Date: 2019-04-05
SYSU CMU SHUNDE INT JOINT RES INST +1
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[0004] Based on this, the present invention aims at the problems that the existing reconstruction algorithm is not accurate enough, fast enough, and requires a large number of projections, and provides a sparse reconstruction method with L1 norm minimization and weight reset at a low sampling rate and system
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[0018] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0019] In one example, see figure 1 , a kind of L1 norm minimization sparse reconstruction method of weight reset under a kind of low sampling rate, described method comprises the following steps:
[0020] S100 acquires an original signal, and processes the original signal using a gradient projection-based sparse reconstruction algorithm to obtain a convex optimization problem model.
[0021] In the field of compressed sensing technology, the sparse reconstruction algorithm based on gradient projection is a reconstruction algorithm based on the solution of the minimized L1 norm. When using this algorithm for image processing, the image reconstruction effect is relatively impressive on the whole, and it maintains The reconstruction quality is also guaranteed at a higher sampling rate. The main idea of the algorit...
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The invention relates to an L1 norm minimization sparse reconstruction method and an L1 norm minimization sparse reconstruction system based on weight resetting under low sampling rate. The method comprises the following steps: acquiring an original signal, and processing the original signal through use of a sparse reconstruction algorithm based on gradient projection to get a convex optimization problem model; using an iterative solution method to solve the convex optimization problem model, setting the weight of each iteration value according to a weight resetting formula to get a weighted convex optimization problem model, wherein the weight resetting formula is a formula for calculating the corresponding weight according to each iteration value, a core parameter Beta and a preset parameter Tau; and sparsely reconstructing the original signal according to the weighted convex optimization problem model. According to the method, through weight resetting, the sampling rate of the traditional reconstruction algorithm is reduced, the time needed for reconstruction is reduced, and higher reconstruction precision is achieved for original signals of the same sparse degree.
Description
technical field [0001] The present invention relates to the technical field of information processing, in particular to a method and system for sparse reconstruction with L1 norm minimization and weight reset at a low sampling rate. Background technique [0002] In traditional technology, the collection of information is based on Shannon’s sampling theorem, that is, the sampling frequency should be greater than twice the highest frequency of the signal, so that the collected data can completely reconstruct the original information, but as our demand for information increases The bandwidth of the signal is increasing rapidly, so the information processing method based on the above sampling theorem requires the sampling processing instrument to have a higher sampling rate and faster processing speed, which will inevitably increase the cost of information acquisition and information processing , especially for the current situation of collecting and processing signals all the t...
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IPC IPC(8): H03M7/30
CPCH03M7/3062
Inventor 赖基泉陈雪晨郑坚泽
Owner SYSU CMU SHUNDE INT JOINT RES INST