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Research And Application On Multi-Sensory Image Fusion At Pixel Level

Posted on:2006-11-19Degree:DoctorType:Dissertation
Country:ChinaCandidate:L L LiFull Text:PDF
GTID:1118360182469167Subject:Pattern Recognition and Intelligent Systems
Abstract/Summary:PDF Full Text Request
With the development of image sensor technology, multi-sensor image fusion has attracted many attentions in image analysis, computer vision and remote sensing, which is widely applied in a variety of fields such as automatic target recognition, intelligent robots, remote sensing, medical image analysis and manufacturing. Pixel level multi-sensor image fusion can obtain more original information, and has a better detection performance and application. Therefore, it is the basis of the image fusion on other levels. The researches in this thesis were partly supported by three national research grants and focused on the techniques and applications of pixel level multi-sensor image fusion. The main contents of the thesis include: Multi-sensor image registration is the prerequisite of pixel level multi-sensor image fusion. The error of image registration significantly affects the result of image fusion. First, image registration techniques are reviewed, and their advantages and drawbacks are analyzed. To solve these problems, a multi-sensor image registration method based on Harris corner was proposed. The first step is Harris corner detection in the reference and sensed images. At the second step, the corner points are matched based on neighborhood correlation and affine invariance of Mahalanobis distance. The final step, based on the set of correctly matched corner point pairs, the accurate transformation parameters between the reference and sensed images are estimated. Experiments demonstrated that our image registration method can register images with a big rotation angle, shift, or gray level difference, and have both high registration accuracy and speed. Discrete wavelet transform has a shift variance problem. To solve this problem, a shift invariant fusion approach based on discrete wavelet frames was developed. The method defines two fusion measurements, improved-neighborhood-entropy for lowpass subband and across-band-neighborhood-space-frequency for highpass subbands. It effectively extracts salient features at different scales and directions and fuse them. Experimental results demonstrated that satisfactory results were obtained using the proposed method. In order to satisfy the requirement of more directions in image fusion, based on the study of steerable pyramid's principle and characters, another shift invariant fusion approach based on steerable pyramid was presented. This method makes use of the multi-orientation and invariant property of steerable pyramid to obtain better results than the method based on discrete wavelet frames. An image fusion approach based on dual tree complex wavelet transform was proposed in Chapter 5. This method provides approximate shift invariance and good directional selectivity with limited redundancy. It obtained a good fusion performance and dramatically decreased the computational complexity. How to assess image fusion performance is important for evaluating image fusion method. First, the evaluation measurements proposed are reviewed, and the qualitative and quantitative comparison rules are set up. Furthermore, use the proposed methods to evaluate the methods developed in this thesis. Finally, the effect of the size of neighborhood window, decomposition level and fusion rules on the performance were investigated, and some conclusions were concluded.
Keywords/Search Tags:Image fusion, image registration, pyramid, wavelet transform, wavelet frame, steerable pyramid, dual tree complex wavelet transform
PDF Full Text Request
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