Pseudo Color Fusion of Infrared and Visible Images Based on the Rattlesnake Vision Imaging System

Wang, Yong; Liu, Hongqi; Wang, Xiaoguang

doi:10.1007/s42235-021-00127-3

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Third, the improved image fusion model based on rattlesnake double-mode cells proposed by Wang et al [23] is taken as one of the comparative experiments. In order to facilitate the identification of comparison methods in subsequent evaluation, it is denoted as Wang here.…”

Section: Resultsmentioning

confidence: 99%

“…Li [17] Zhang [18] Wang [23] LatLRR [24] GTF [25] MSVD [26] Table 3 and Figure 17 show the data table and corresponding line graph of the information entropy evaluation index obtained by experimental comparison of seven other fusion methods and the improved method Our using seven TNO dataset image pairs, respectively. By analyzing the information in the data table and line chart, it can be found that 1/7 of the information entropy best values are generated using the method proposed by Li et al [17], 1/7 of the information entropy best values are generated using the GTF method, 2/7 of the information entropy best values are generated using the method proposed by Wang et al [18], and 3/7 of the information entropy best values are generated using the improved method Our.…”

Section: Table 2 Spatial Frequency (Tno)mentioning

confidence: 99%

“… show the fusion results obtained by the proposed improved algorithm and the corresponding comparative experiment using the source image simulation of seven groups of the TNO dataset. In these results, (1) and (2) are visible and infrared source images, respectively; (3) is the fusion result of Li's method[17]; (4) is the fusion result of Zhang's method[18]; (5) is the fusion result of Wang's method[23];(6) is the fusion result of GTF's method[24];(7) is the fusion result of the LatLRR method[25];(8) is the fusion result of the MSVD method[26];(9) is the fusion result of the FPDE method[27];(10) is the fusion result of the improved method Our.…”

mentioning

confidence: 99%

“…Comparison of experimental results of the first set of graphs in the TNO dataset. (1) visible image; (2) infrared image;(3) Li[17]; (4) Zhang[18]; (5) Wang[23]; (6) GTF[24]; (7) LatLRR[25]; (8) MSVD[26]; (9) FPDE[27];(10) Our.…”

mentioning

confidence: 99%

“…Standard deviation (MSRS). Line chart of standard deviation (MSRS dataset)[17,18,[23][24][25][26][27].…”

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confidence: 99%

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Image Fusion Method Based on Snake Visual Imaging Mechanism and PCNN

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The process of image fusion is the process of enriching an image and improving the image’s quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today’s deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes.

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Section: Resultsmentioning

confidence: 99%