A simple and efficient fusion framework for surveillance images
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- Category: Information technologies, systems analysis and administration
- Last Updated on 19 January 2017
- Published on 19 January 2017
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Authors:
Lili Chen, Laboratory of Intelligent Information Processing, Suzhou University, Suzhou, China
Hongjun Guo, The Key Laboratory of Intelligent Computing & Signal Processing of MOE, Anhui University, Hefei, China
Abstract:
Purpose. Aiming at solving the fusion issue of surveillance images, a simple and efficient fusion framework using block compressed sensing sampling (BCSS) is proposed in this paper, which consists of two fusion methods using basic-BCSS and sliding-BCSS respectively.
Methodology. With the superiority of low sampling ratio and low computational complexity, compressed sensing (CS) theory is widely used in signal processing. The basic-BCSS is a basic version of block based CS, in which the source image is divided into distinct blocks, and the sliding-BCSS is a modified version of basic-BCSS proposed for the first time, in which the image is divided into small sliding blocks for each pixel with appropriate padding. The basic idea of the fusion framework is to select the blocks or pixels with greater L2-norm of the BCSS measurement outputs of the divided blocks in spatial domain.
Findings. The fusion framework is tested on three pairs of grayscale surveillance images, including infrared and visible images, and millimeter-wave and visible images, and compared with several traditional fusion methods. Experimental results demonstrate that the proposed fusion framework can significantly improve the fusion quality and speed simultaneously.
Originality. A simple and efficient fusion framework using BCSS in spatial domain is proposed for the first time.
Practical value. It has a certain practical meaning for real-time surveillance applications.
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