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Kernel regression-based image compression sensing reconstruction method

An image compression and kernel regression technology, applied in the field of image processing, can solve problems such as affecting the effect of image reconstruction and ignoring the correlation of image blocks, and achieve the effect of improving reconstruction quality and quality.

Active Publication Date: 2012-01-25
XIDIAN UNIV
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Problems solved by technology

However, in this block image compression perception, since the image blocks are independently reconstructed, the correlation between the image blocks is ignored, which often leads to the block effect of the reconstructed image, which affects the reconstruction of the image. Effect

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  • Kernel regression-based image compression sensing reconstruction method
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  • Kernel regression-based image compression sensing reconstruction method

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[0031] Refer to attached figure 1 , the concrete steps of the present invention are as follows:

[0032] Step 1. Block the scene image and use the observation matrix to obtain the corresponding observation vector

[0033] Right as attached image 3 , Figure 4 , Figure 5 , Figure 6 The scene X shown is subjected to block-compressed imaging, and the observation matrix is ​​used to observe the small image block x in the local area of ​​X, where the size of the small image block is 16×16, and the corresponding observation vector is: y=As; where A is the observation matrix, where a random Gaussian matrix is ​​taken, and s is the result of converting the image block x into a column vector;

[0034] Step 2. Use the OMP reconstruction algorithm to obtain the initial reconstructed scene image

[0035] 2a) For the observation matrix y obtained in step 1, use the OMP algorithm to solve the formula: Get the sparse decomposition coefficient α corresponding to the image small blo...

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Abstract

The invention discloses a kernel regression-based image compression sensing reconstruction method, which mainly solves the problem of reduced quality of a reconstructed image caused by mutually independent reconstruction of each image block and lack of considering linkage between the image blocks existing in the conventional method. The method comprises the following steps of: partitioning an input scene image; performing preliminary reconstruction on the image blocks by using an orthogonal matching pursuit (OMP) algorithm; then performing a kernel regression method on the image to obtain a local gray matrix of the image small blocks; weighing by using neighborhood image blocks to obtain a non-local gray matrix of the image small blocks; and finally, solving the final reconstruction imagesmall blocks through least square by using the local gray matrix and the non-local gray matrix of the image small blocks, and repeating the operation on all the image small blocks to obtain the finalreconstructed image. In the invention, both the reconstruction effects of various natural images and cartoon images can be improved under different sampling rates; and the method can be used for compressing high-resolution recovery or reconstruction of various low-resolution images under observation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image reconstruction method under the theoretical framework of compressed sensing, which can be used for high-resolution recovery or reconstruction of various low-resolution images under compressed observation. Background technique [0002] Compressed sensing is a new theory about signal transmission and storage developed in the field of signal processing in recent years. It breaks through the limitation of sampling rate in traditional Nyquist sampling, and can realize accurate perception of information at low sampling rate. The traditional image compression sensing reconstruction method is to directly compress and observe the overall image, and then use an optimization algorithm to restore the image. Due to the large amount of information contained in the large scene image, the observation matrix is ​​too large, resulting in high computational complexity. Recently, bl...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 杨淑媛焦李成周宇刘芳邓小政侯彪吴赟张小华
Owner XIDIAN UNIV
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