Peizhou Yan1, Jiancheng Zou2,*, Zhengzheng Li1, Xin Yang3
Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 213-225, 2021, DOI:10.32604/iasc.2021.016201
Abstract Within the application of driving assistance systems, the detection of driver’s facial features in the cab for a spectrum of luminosities is mission critical. One method that addresses this concern is infrared and visible image fusion. Its purpose is to generate an aggregate image which can granularly and systematically illustrate scene details in a range of lighting conditions. Our study introduces a novel approach to this method with marked improvements. We utilize non-subsampled shearlet transform (NSST) to obtain the low and high frequency sub-bands of infrared and visible imagery. For the low frequency sub-band fusion, we incorporate the local average… More >
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