Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

doi:10.1007/s12200-015-0440-z

Front. Optoelectron.

2015, Vol. 8

Issue (2) : 195-202 https://doi.org/10.1007/s12200-015-0440-z

RESEARCH ARTICLE

Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

Shaosheng DAI,Zhihui DU,Haiyan XIANG,Jinsong LIU(

)

Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Abstract

Aiming at solving the problem of low resolution and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism (HVPM) was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets (POCS) reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super-resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approximately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.

Keywords human vision processing mechanism (HVPM) projection onto convex sets (POCS) super-resolution infrared image reconstruction algorithm

Corresponding Author(s): Jinsong LIU

Just Accepted Date: 04 February 2015 Issue Date: 24 June 2015

Cite this article:

Shaosheng DAI,Zhihui DU,Haiyan XIANG, et al. Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism[J]. Front. Optoelectron., 2015, 8(2): 195-202.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-015-0440-z
https://academic.hep.com.cn/foe/EN/Y2015/V8/I2/195

Fig.1 Retina cell layer structure

Fig.2 Super-resolution method simulating retina visual processes

Fig.3 Reconstruction renderings of low contrast infrared image. (a) is the original HR image; (b) is the LR image sequences, whose size is 154 × 114; (c) is the local image of one LR image, which is magnified to the size of original image; (d) is the processed image by bilinear interpolation; (e) is the processed image by traditional POCS reconstruction algorithm; (f) represents processed image by the proposed algorithm

Tab.1 Evaluation indexes of the low contrast image after processing

Fig.4 Reconstruction renderings of noise infrared image. (a) is the original HR image; (b) is the LR image sequences, whose size is 150 × 116; (c) is the local image of one LR image; (d) is the processed image by bilinear interpolation; (e) is the processed image by traditional POCS reconstruction algorithm; (f) represents processed image by the proposed algorithm

Tab.2 Evaluation index of the high noise image after processsing

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