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	Spatial frequency domain imaging technology based on Fourier single-pixel imaging 期刊论文 OAI收割 JOURNAL OF BIOMEDICAL OPTICS, 2022, 卷号: 27 作者: Ren, Hui M.; Deng, Guoqing; Zhou, Peng; Kang, Xu; Zhang, Yang \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2022/03/21 Fourier single-pixel imaging spatial frequency domain optical properties compressed sensing
	A Method to Evaluate Error Correction Ability of Computer Controlled Optical Surfacing Process 期刊论文 OAI收割 OPTICAL REVIEW, 2014, 卷号: 21, 期号: 3, 页码: 280-285 作者: Wang, Jia; Fan, Bin; Wan, Yongjian; Shi, Chunyan; Zhuo, Bin 收藏 \| 浏览/下载：35/0 \| 提交时间：2015/07/10 CCOS process power spectral density convolution model spatial frequency domain
	Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China Wu Z.-G.; Wang M.-J.; Han G.-L. 收藏 \| 浏览/下载：72/0 \| 提交时间：2013/03/25 Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
	Displacement estimation by the phase-shiftings of fourier transform in present white noise (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wu Y.-H. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 Displacement estimation is a fundamental problem in Real-time video image processing. It can be typically approached by theories based on features in spatial domain. This paper presents an algorithm which improves the theory for estimating the moving object's displacement in spatial domain by its Fourier transform frequency spectrum. Because of the characters of Fourier transform the result is based on all the features in the image. Utilizing shift theorem of Fourier transform and auto-registration the algorithm employs the phase spectrum difference in polar coordinate of two frame images sequence with the moving target1 2. The method needn't transform frequency spectrum to spatial domain after calculation comparing with the traditional algorithm which has to search Direc peak and it reduces processing time. Since the technique proposed uses all the image information including all the white noise in the image especially and it's hard to overcome the aliasing from noises but the technique can be an effective way to analyze the result in little white noise by the different characters between high and low frequency bands. It can give the displacement of moving target within 1 pixel of accuracy. Experimental evidence of this performance is presented and the mathematical reasons behind these characteristics are explained in depth. It is proved that the algorithm is fast and simple and can be used in image tracking and video image processing.