Multi-view compressed sensing image reconstruction method

Abstract

The invention discloses a multi-view compressed sensing image reconstruction method. The multi-view compressed sensing image reconstruction method includes: receiving measured values of various view-angle images at a receiving end and independently reconstructing various view-angle images; performing DE (disparity estimation) and DC (disparity compensation) on the basis of blocks among the reconstructed images and acquiring predicted images of each view angle; solving a joint reconstruction problem set for each view angle by means of the predicted images and the measured values and determining the final reconstruction images of various view angles. In the set joint reconstruction problem, not only can sparsity characteristics of residual errors between the view-angle images and the predicted images in a transform domain be taken into consideration, but the sparsity characteristics of the view-angle images in the transform domain is further considered. Meanwhile, the joint reconstruction problem is solved into an alternative iteration problem of view-angle reconstruction images and residual-error images and can be solved by multiple times of iteration. By the multi-view compressed sensing image reconstruction method, performance and effect of the image reconstruction can be effectively improved.

Description

technical field [0001] The present application relates to image coding technology, in particular to a method for reconstructing multi-view compressed sensing images in the image coding technology. Background technique [0002] Compressed Sensing (CS) is an emerging theoretical system that has received extensive attention in the field of signal processing in recent years. The theory states that if a signal to be sampled is sparse (or sparse in a transform domain), then the possibility of accurately recovering the original signal from its limited uncorrelated measurements is extremely high. The introduction of CS theory has greatly enriched and developed the signal acquisition theory, and provided new technologies and new ideas for research in other related fields. Because the computational complexity of the measurement process of CS is low, and the reconstruction process needs to iteratively solve a relatively high-complexity optimization problem, its theoretical structure i...

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Problems solved by technology

However, the method of residual reconstruction cannot take advantage of the spatial characteristics of the image itself, and the performance of this method still has room for improvement

[0008] In summary, at the receiving end, in order to improve the quality of independently reconstructed images, it is necessary to make full use of the correlation between images from different perspectives; the performance of existing methods needs to be further improved

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