A multi-scale coding and multi-constrained super-time resolution compressed sensing reconstruction method

Abstract

The invention discloses a time compression reconstruction method based on a multi-scale coded aperture and a multi-regular constraint, which utilizes the motion characteristic of a target scene as a fusion basis and adopts a multi-scale coded aperture and a multi-regular constraint reconstruction method to realize super-time resolution restoration of a compressed perceptual video sequence image ofa fast moving scene. This method can guarantee the definition of the moving foreground and static background of the target, and improve the efficiency of the reconstruction algorithm. According to the sparsity of the target scene, the algorithm uses the multi-scale observation matrix to realize the twice coding of the aperture, which can realize the fast reconstruction of CACTI. Using the sparseproperty of the target scene in transform domain as a priori knowledge, the reconstruction constraint is constructed, ADMM algorithm is more robust to noise and motion blur than the existing reconstruction algorithm, which can improve the reconstruction effect of video compressed sensing under high noise or high frame rate.

Description

technical field [0001] The invention relates to the technical field of light field modulation and computational imaging, in particular to a time compression reconstruction method based on multi-scale coded apertures and multiple regular constraints. Background technique [0002] The tracking imaging system for high-speed moving targets is limited by the pixel size of the sensor and the readout circuit, which is prone to motion blur caused by long exposure time, or time undersampling caused by low camera frame rate, thus causing motion aliasing . The proposal of CACTI time-compressed aperture coded imaging method (CACTI, Coded Aperture Compressive Temporal Imager) effectively improves the ability to capture high-speed moving targets, and provides an effective means for the recording and analysis of key events. This method has become an important research in the field of compressed sensing. direction. [0003] CACTI is a fast imaging method based on the principle of aperture...

AI Technical Summary

Problems solved by technology

However, the compared GAP algorithm is not optimized for specific scenarios, especially the weighting coefficient adopts the default value, so the algorithm needs to be selected according to the actual usage scenario

In addition, the GMM algorithm using parallel computing is faster than the GAP algorithm, but for serial computing occasions, the speed is still slower than the GAP algorithm

[0009]Comprehensive analysis, the current CACTI imaging method has the following problems: First, due to the use of motion-coded aperture, the system needs to add a high-resolution mask or spatial light modulator, Reduces the light flux of the imaging system, making the imaging results more sensitive to noise

Second, the reconstruction process of existing algorithms mostly utilizes intra-frame information, and does not make full use of inter-frame redundant information

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