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Light field image compression perception method based on LDPC matrix

A light field image and compressed sensing technology, which is applied in image coding, image data processing, instruments, etc., can solve the problem of large storage space, reduce the storage space of light field image transmission, increase the speed of reconstruction, and achieve high probability The effect of refactoring

Inactive Publication Date: 2018-07-03
TIANJIN UNIV
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Problems solved by technology

[0006] Although the light field camera can realize the function of shooting first and then focusing, but because the light field image records the position and direction of each beam of light entering the lens, it contains three-dimensional light field information and stores a large amount of information, making it occupy more storage space

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  • Light field image compression perception method based on LDPC matrix

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Embodiment Construction

[0022] Embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0023] The overall thinking of the present invention: first, carry out "core set" training on the light field image blocks, and select a sample set more suitable for dictionary training; then obtain a K-SVD dictionary with better performance and more in line with sample characteristics through online sparse coding , using a new type of LDPC matrix to down-sample the sparse light field image sample blocks; finally, the OMP reconstruction algorithm is used to restore the light field image signal processing results.

[0024] Such as figure 1 As shown, it is a schematic diagram of the overall flow of the LDPC matrix-based light field image compression sensing method. The present invention comprises the following steps:

[0025] Step 1. Establish a sample data set, that is, select 5 groups of light field images from the open source general light f...

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Abstract

The invention discloses a light field image compression perception method based on an LDPC matrix; the method comprises the following steps: 1, building a sample data set; 2, using a Matlab to processall light field image block files in the sample data set, selecting several sets of images with a big variance to serve as the follow up test materials; 3, further obtaining a trained K-SVD dictionary and using same as a selected training set; 4, carrying out online sparse coding treatment for the K-SVD dictionary trained in step 3; 5, forming a measuring matrix; 6, using an OMP algorithm to reconstruct downsampled light field image data; 7, obtaining reconstruction signals. Compared with the prior art, the method is simple and efficient, thus reducing the light field image transmission storage space, improving the reconstruction speed at certain level while ensuring accuracy, and realizing the light field signal downsampling process and high probability reconstruction.

Description

technical field [0001] The invention relates to various fields such as compressed sensing technology and light field image processing technology, and in particular to a light field image compressed sensing method. Background technique [0002] The Nyquist sampling theorem states that the original signal can be accurately reconstructed from the sampled signal only when the sampling rate is more than twice the signal bandwidth. It can be seen that bandwidth is the essential requirement of Nyquist sampling theorem for sampling. However, with the increase of people's demand for information, the signal bandwidth is getting wider and wider, and the signal processing framework based on this has higher and higher requirements for sampling rate and processing speed. Therefore, people try to explore whether other transform spaces can be used to describe signals to establish a new theoretical framework for signal description and processing, so that the signal can be sampled at a sampl...

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/00
Inventor 刘昱翟丽
Owner TIANJIN UNIV
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