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	Automatic Dewarping of Retina Images in Adaptive Optics Confocal Scanning Laser Ophthalmoscope 期刊论文 OAI收割 IEEE Access, 2019, 卷号: 7, 页码: 59585-59599 作者: Chen, Hao; He, Yi; Wei, Ling; Li, Xiqi; Zhang, Yudong \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2021/05/06 Retina image adaptive optics confocal scanning laser ophthalmoscope image dewarping image registration nonlinear scale space Gaussian scale space
	Improved Kernel Correlation Filter Tracking with Gaussian scale space 会议论文 OAI收割 International Symposium on Infrared Technology and Application and the International Symposiums on Robot Sensing and Advanced Control, Beijing, May 9-11, 2016 作者: Tan SK(谭舒昆); Liu YP(刘云鹏); Li YC(李义翠) 收藏 \| 浏览/下载：22/0 \| 提交时间：2016/09/13 visual object tracking kernel correlation filter Gaussian scale space
	Human visual system consistent quality assessment for remote sensing image fusion 期刊论文 OAI收割 ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2015, 卷号: 105, 页码: 79-90 作者: Liu, Jun; Huang, Junyi; Liu, Shuguang; Li, Huali; Zhou, Qiming \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2018/03/15 Image Fusion Quality Assessment Human Visual System Gaussian Scale Space Spatial Quality Index Spectral Quality Index
	A line mapping based automatic registration algorithm of infrared and visible images 会议论文 OAI收割 5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013 作者: Ai R(艾锐); Shi ZL(史泽林); Xu DJ(徐德江); Zhang CS(张程硕) 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/12/26 There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit linesegment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.
	A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.
	Dyadic scale space 期刊论文 OAI收割 PATTERN RECOGNITION, 1997, 卷号: 30, 期号: 2, 页码: 339-352 作者: Cong, G; Ma, SD \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2018/10/10 Multiscale Signal Processing Scale Space Dyadic Scale Space Interpolation Feature Detection Approximating Gaussian Zero-crossing