Research On Infrared And Visual Image Fusion

With the development of sensor technology, the infrared imaging sensor and visible light imaging sensor have played an increasingly significant role in enhancing the performance of some civilian and military applications. Due to the characteristics and limitations, it is difficult to complete the task using single imaging sensor in some environment. The focus of image fusion is to integrate complementary information from multiple images of the same scene, which comes from infrared imaging sensor and visible light imaging sensor, to create a single composite that contains all the typical features of the source images. The fused image will thus be more informative and suitable for human visual perception.In this thesis, infrared and visual image fusion algorithm are studied systematically, and a number of infrared and visual image fusion algorithms are proposed, which based on multi-resolution analysis tools, such as Dual-Tree Complex Wavelet Transform(DTCWT), Non-subsampled Contourlet Transform(NSCT), and combine with an amount of simulation experiments.Firstly, imaging properties of two types of sensors are introduced, which are infrared imaging sensor and visible light imaging sensor. The main-streamed techniques are compared and categorized to two categories including spatial domain analysis and multi-scale analysis. The quality evaluation method for image fusion has been discussed and summarized.Secondly, The NSCT is a fully shift-invariant, multi-scale, and multi-direction expansion that has a fast implementation. In date, the laws of image fusion algorithm based on NSCT underutilize the correlation of the bandpass directional subband coefficients. Considering the relationship of NSCT bandpass directional subband coefficients, a new spatial correlation function is defined, which can measure the edge and texture of image for infrared and visual image fusion. And a novel method for the fusion of infrared and visual images is presented, which based on the correlation of the bandpass directional subband coefficients of NSCT. Experimental results show that the algorithm can effectively integrate the edge and texture information of infrared and visual images.Thirdly, a novel algorithm is proposed for infrared and visual image fusion, which is based on NSCT and image segmentation. At first, watershed transform is applied to the gradient surface of infrared image. Then, infrared and visual images are decomposed by NSCT. Next, different laws are applied to different regions for fusion, which are based on region characteristics. Comparing with other fusion algorithms based on multi-scale analysis, the proposed approach outperforms in both important and region structure information.And then, we study the structural similarity theory, and construct infrared and visual image fusion framework based on region classified and multi-scale transform. This method could integrate hot target information and spatial structure information efficiently, and the fused image will be more informative and suitable for human visual perception.Finally, to assess the performance of the design fusion framework, two algorithms are presented. They are infrared and visual image fusion algorithm based on NSCT and region classified, and infrared and visual image fusion algorithm based on DTCWT and region classified. The designed fusion framework has proven to be efficient in infrared and visual image fusion as we showed in this paper. The both algorithms has proven to be more accurately in capture the hot target and significant region structure information from the infrared and visual images, the fused images are clearer and more informative content.In summary, the thesis systematically studies the infrared and visual image fusion technology. And several novel fusion algorithms for infrared and visual image fusion are proposed. Simulation experiments confirm that the proposed algorithms have better fusion performance.