Research on iterative repair algorithm of Hyperchaotic image based on support vector machine

Xin Li; Ziguan Cui; Linhui Sun; Guanming Lu; Debnath Narayan

doi:10.3934/dcdss.2019083

Article Contents

2019, Volume 12, Issue 4&5: 1199-1218. Doi: 10.3934/dcdss.2019083 open access

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Research on iterative repair algorithm of Hyperchaotic image based on support vector machine

1.
College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Department of Computer Science, Winona State University, Winona, MN 55987, USA

* Corresponding author: Xin Li
* Corresponding author: Xin Li

Received: July 31, 2017

Revised: November 30, 2017

Published: March 2019

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Abstract

The damaged area of the hyperchaotic image is prone to lack of texture information. It needs to make image restoration design to improve the information expression ability of the image. In this paper, an iterative restoration algorithm of hyperchaotic image based on support vector machine is proposed. The sample blocks in the damaged region of hyperchaotic images are divided into smooth mesh structures according to block segmentation method, and the neighborhood pixels of which points need to repair are ranked efficiently according to gradient values. According to the edge fuzzification features, the position of the important structural information of the damaged area is located. A multi-dimensional spectral peak search method is applied to construct the information feature subspace of image texture, so as to find the best matching block for restoring the damaged region of hyperchaotic image. Considering the features of structural information and texture information, the maximum likelihood algorithm is used to reconstruct the pixel elements in the image region by piecewise fitting. Through the support vector machine algorithm, the image iterative restoration is carried out. The simulation results show that the restoration method for hyperchaotic image can achieve effective restoration of image damaged area, the quality of restorationed image is better, and the computation speed is fast. The image restoration method can effectively ensure the visual effect of the reconstructed image.

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Mathematics Subject Classification: 46N30.

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Figure 1. The principle diagram of block segmentation of hyperchaotic image

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Figure 2. Comparison of image "Cow" restoration effect

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Figure 3. Comparison of image "Rabbit" restoration effect

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Figure 4. Comparison of image "Golf" restoration effect

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Figure 5. Comparison of image "Wall" restoration effect

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Figure 6. Comparison of image "Stripes" restoration effect

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Table 1. Comparison of experiment data in the first group

	SVM iterative restoration algorithm		Criminisi algorithm
Image data set	Computing time $T1$(S)	The signal to noise ratio of the restored image: V($dB$)	Computing time $T2$(S)	The signal to noise ratio of the restored image: V($dB$)	Ratio of restoration restoration time $R = T2/T1$	Comparison of signal-to-noise ratio: $(U-V)/$ $ V(\%)$
Cow($512\times 384$)	209.656	22.564	1545.233	21.544	8.24	$\uparrow 3.54$
Rabbit($402\times 336)$	20.53	33.241	174.324	33.232	8.35	$\uparrow 1.56$
Golf($262\times 350)$	12.234	30.665	85.245	30.344	7.02	$\downarrow 0.57$
Wall($190\times 186)$	6.323	28.543	46.314	30.454	7.34	$\downarrow 5.96$
Stripes($176\times 155)$	2.453	42.032	16.543	43.445	6.64	$\downarrow 3.76$

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Table 2. Comparison of experiment data in the second group

	SVM iterative restoration algorithm		Criminisi algorithm
Image data set	Computing time $T1$(S)	The signal to noise ratio of the restored image: V($dB$)	Computing time $T2$(S)	The signal to noise ratio of the restored image: V($dB$)	Ratio of restoration restoration time $R = T2/T1$	Comparison of signal-to-noise ratio: $(U-V)/$ $V(\%)$
Cow($512\times 384)$	142.354	22.545	1655.221	21.444	11.85	$\uparrow 1.24$
Rabbit($402\times 336)$	15.545	32.740	157.545	33.464	11.55	$\downarrow 1.45$
Golf($262\times 350)$	7.344	30.469	85.565	30.443	11.45	$\uparrow 0.51$
Wall($190\times 186)$	4.455	30.908	46.877	30.356	9.56	$\downarrow 0.56$
Stripes($176\times 155)$	1.666	41.876	16.54	43.676	9.65	$\downarrow 4.93$

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Table 3. Comparison of experiment data in the third group

	SVM iterative restoration algorithm		Criminisi algorithm
Image data set	Computing time $T1$(S)	The signal to noise ratio of the restored image: V($dB$)	Computing time $T2$(S)	The signal to noise ratio of the restored image: V($dB$)	Ratio of restoration restoration time $R = T2/T1$	Comparison of signal-to-noise ratio: $(U-V)/$ $V(\%)$
Cow($512\times 384)$	109.464	22.454	1232.243	22.976	11.63	$\uparrow 1.12$
Rabbit($402\times 336)$	13.045	31.554	163.465	32.566	12.34	$\downarrow 2.67$
Golf($262\times 350)$	6.454	30.464	81.354	30.654	13.43	$\uparrow 0.34$
Wall($190\times 186)$	3.833	28.578	40.456	28.533	10.46	$\downarrow 0.45$
Stripes($176\times 155)$	1.354	40.665	15.566	43.355	9.76	$\downarrow 5.45$

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