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Image Fusion Base on Overcomplete Dictionary Sparse Representation

Author: ZhuYaPing
Tutor: LiuFang
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Sparse Representation Overcomplete Dictionary Image Fusion
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 157
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In recent years, image fusion has become an important technology in the field of image processing and computer vision. Image fusion is to optimally extract the important complementary information from different channels by the technologies of image processing and computer vision, and eventually to get a high-quality image in which all the important information from every channel is fused. The information is a few frames of image data which are got from multi-source channels on the same object or area. Fused image is more comprehensive,accurate and detailed about the information description.Image representation is the premise of image processing and also the basic problem in the field of image processing. The validity of image representation refers to the capability of capturing more important information in the image with less data, which is sparse representation. Sparse representation has early been used in the compression, denoising and fusion problems of signal processing and approximation theory. Image itself is not sparse, but it has a sparse representation under a base or a framework. Multi-scale wavelet transform gives sparse representation of the natural image, so it also has a large number of applications in the image fusion.Compared with multi-scale wavelet transform, overcomplete dictionary is able to make a more sparse representation of images. Therefore an image fusion algorithm based on overcomplete dictionary is proposed in this paper, in which block processing is done to the images, and fusion images’complementation is fully taken into account in the fusion process. The fusion rule can guarantee the fused image blocks include complementary information and important information of the images to be fused. Then information entropy or the spatial frequency is adopted to reach the final fused image blocks from the fused image blocks. Both theoretical analysis and experimental results demonstrate that the fusion rule in this paper is reasonable and effective. Simulation on medical images and remote sensing images show that image fusion based on overcomplete dictionary sparse representation proposed in this paper is more effective than that based on multi-scale wavelet transform.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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