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【期刊论文】Image fusion using non-separable wavelet frame
敬忠良, Hong Wang, Zhongliang Jing, and Jianxun Li
CHINESE OPTICS LETTERS September 20, 2003 Vol. 1, No.9,-0001,():
-1年11月30日
In this paper, an image fusion method is proposed based on the non-separable wavelet frame (NWF) for merging a high-resolution panchromatic image and a low-resolution multispectral image. The low- frequency part of the panchromatic image is directly substituted by multispectral image. As a result, the multispectral information of the multispectral image can be preserved effectively in the fused image. Due to multiscale method for enhancing the high-frequency parts of the panchromatic image, spatial information of the fused image can be improved. Experimental results indicate that the proposed method outperforms the intensity-hue-saturation (IHS) transform, discrete wavelet transform and separable wavelet frame in preserving spectral and spatial information.
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敬忠良, Shijun Zhang, Zhongliang Jin, and Jianxun Li
CHINESE OPTICS LETTERS Vol. 3, No.1 January 10, 2005,-0001,():
-1年11月30日
The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-orgamzing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate. robust adaptability, quick training, and better generalization of the proposed method are achieved.
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【期刊论文】Pixel-clarity-based multifocus image fusion
敬忠良, Zhenhua Li, Zhongliang Jing, and Shaoyuan Sun
CHINESE OPTICS LETTERS February 10, 2004 Vol. 2, No.2,-0001,():
-1年11月30日
Due to the limited depth-of-field of optical lenses, it is difficult to get an image with all objects in focus. One way to overcome this problem is to take several images with different focus points and combine them into a single composite which contains all the regions full focused. This paper describes a pixel-clarity-based multifocus image fusion algorithm. The characteristic of this approach is that the pixels of the fused image are selected from the clearest pixels in the input images according to pixel clarity criteria. For each pixel in the source images, the pixel clarity is calculated. The fusion procedure is performed by a selection mode according to the magnitude of pixel clarity. Consistency verification is performed on the selected pixels. Experiments show that the proposed algorithm works well in multifocus image fusion.
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【期刊论文】Image fusion based on expectation maximization algorithm and steerable Pvramid
敬忠良, Gang Liu, Zhongliang Jing, Shaoyuan Su, Jianxun Li, Zhenhua Li , and Henry Leung
CHINESE OPTICS LETTERS July 10, 2004 Vol. 2, No.7,-0001,():
-1年11月30日
In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid. The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations. Experimental results show that the proposed method outperforms conventional image fusion methods.
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【期刊论文】Neural network-based state fusion and adaptive tracking for maneuvering targets ☆
敬忠良, Zhongliang Jing*
Communications in Nonlinear Science and Numerical Simulation 10(2005)395-410,-0001,():
-1年11月30日
An adaptive algorithm for tracking maneuvering targets is proposed. This algorithm is implemented with two filters and a multilayer feedforward neural network using state fusion, together with the current statistic model and adaptive filtering. The neural network fuses automatically all the state information of the two filters and tunes adaptively the system variance for one of the two filters to adapt to different target maneuvers when the two filters track the same maneuvering target in parallel. Simulation results show that the adaptive algorithm tracks very well maneuvering targets over a wide range of maneuvers with high precision, in both one and three-dimensional cases.
Adaptive filtering, State fusion, Neural network, Target tracking
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