Fusion of infrared and visible images based on saliency scale-space in frequency domain

Yanfei Chen; Nong Sang; Zhiping Dan

doi:10.1117/12.2205005

14 December 2015 Fusion of infrared and visible images based on saliency scale-space in frequency domain

Yanfei Chen, Nong Sang, Zhiping Dan

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130G (2015) https://doi.org/10.1117/12.2205005
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

A fusion algorithm of infrared and visible images based on saliency scale-space in the frequency domain was proposed. Focus of human attention is directed towards the salient targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, visual features are extracted to obtain the input hypercomplex matrix. Secondly, the Hypercomplex Fourier Transform (HFT) is used to obtain the salient regions of the infrared and visible images respectively, the convolution of the input hypercomplex matrix amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale which is equivalent to an image saliency detector are done. The saliency maps are obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. Thirdly, the salient regions are fused with the adoptive weighting fusion rules, and the nonsalient regions are fused with the rule based on region energy (RE) and region sharpness (RS), then the fused image is obtained. Experimental results show that the presented algorithm can hold high spectrum information of the visual image, and effectively get the thermal targets information at different scales of the infrared image.

Citation Download Citation

Yanfei Chen, Nong Sang, and Zhiping Dan "Fusion of infrared and visible images based on saliency scale-space in frequency domain", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130G (14 December 2015); https://doi.org/10.1117/12.2205005

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