Support set and signal value detection based video compressive sensing reconstruction method

Abstract

The invention discloses a support set and signal value detection based video compressive sensing reconstruction method which mainly solves the problem of poor reconstructed image quality in the prior art. The method includes the implementation steps: (1) dividing a video sequence into reference frames and non-reference frames according to image groups; (2) dividing the reference frames and non-reference frames into non-overlapping macro blocks identical in size; (3) subjecting all the macro blocks to compressive sensing measurement; (4) utilizing measurement values as input and updating iteration variables of a reconstructed image; (6) updating a support set and a signal detection value according to updated iteration variables of the reconstructed image; (7) computing a residual error of the reconstructed image according to the signal detection value; (8) judging whether iteration is terminated or not according to constraint conditions of the residual error of the reconstructed image; (9) outputting a reconstructed image signal. The support set and signal value detection based video compressive sensing reconstruction method can improve reconstructed image quality effectively and can be utilized for video image processing.

Description

technical field [0001] The invention belongs to the field of video image processing, relates to a video compression perception reconstruction method, and can be used for video image processing. Background technique [0002] In recent years, with the rapid development of digital signal processing technology, the amount of data to be processed is increasing at an alarming rate. The traditional Nyquist sampling theorem requires that the sampling frequency of the signal should not be lower than twice the maximum frequency of the signal. Higher requirements are put forward for hardware devices with limited signal processing capabilities. In order to break through the traditional signal processing method based on Nyquist sampling theory, a new type of compressed sensing that combines data acquisition and data compression processes into one Theory has become one of the hot spots of research at home and abroad. [0003] The traditional Nyquist theory is suitable for bandwidth-limit...

AI Technical Summary

Problems solved by technology

At present, for the optimal solution of convex problems, Stephen Boyd of Stanford University and others proposed an alternating direction multiplier method ADMM. Although this method requires a relatively small number of measurements, it is slow and the reconstruction quality is relatively low.

In addition, Y.Wang and W.Yin of Cornell University proposed an iterative support set detection method ISD. Although this method further reduces the requirements for the number of measured values, it combines support set update detection and image Sparse reconstruction is processed separately, and the reconstruction quality is not very high

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