机构

• 长春光学精密机械与物... [3]
• 遥感与数字地球研究所 [3]
• 沈阳自动化研究所 [3]
• 西安光学精密机械研究... [1]

采集方式

• OAI收割 [10]

内容类型

• 会议论文 [6]
• 期刊论文 [4]

发表日期

• 2020 [1]
• 2019 [2]
• 2015 [1]
• 2014 [1]
• 2013 [1]
• 2011 [2]
• 更多

学科主题

筛选

浏览/检索结果: 共10条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Infrared stripe correction algorithm based on wavelet decomposition and total variation-guided filtering 期刊论文  OAI收割 JOURNAL OF THE EUROPEAN OPTICAL SOCIETY-RAPID PUBLICATIONS, 2020, 卷号: 16, 期号: 1, 页码: 1-12 作者:  Wang ED(王恩德);  Jiang P(姜平);  Li XP(李学鹏);  Cao H(曹晖)   |  收藏  |  浏览/下载：24/0  |  提交时间：2020/01/11 Focal-plane array  non-uniformity correction  Infrared image  Multi-scale  Destriping   Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization 期刊论文  OAI收割 APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 10, 页码: 1-21 作者:  Peng LY(彭良玉);  Zhu YL(朱亚龙);  Hou XK(侯续奎);  Jiang P(姜平);  Wang ED(王恩德)   |  收藏  |  浏览/下载：74/0  |  提交时间：2019/08/04 non-uniformity correction  focal-plane array  infrared image  destriping   Infrared stripe noise correction algorithm based on multi-scale analysis and one-dimensional vector convolution 会议论文  OAI收割 Shanghai, China, December 20-23, 2019 作者:  Jiang P(姜平);  Wang ED(王恩德);  Feng DQ(冯达其)   |  收藏  |  浏览/下载：24/0  |  提交时间：2020/03/22 Non-uniformity correction  focal-plane array  infrared image  Multi-scale  destriping   An improved histogram matching algorithm for the removal of striping noise in optical remote sensing imagery 期刊论文  OAI收割 OPTIK, 2015, 卷号: 126, 期号: 23, 页码: 15536-15560 作者:  Cao, Biao;  Du, Yongming;  Xu, Daqi 收藏  |  浏览/下载：26/0  |  提交时间：2016/04/20 Striping noise  Destriping  Optical imaging instrument  Detector response function  Improved histogram matching   Destriping remotely sensed data using anisotropic diffusion in wavelet domain 会议论文  OAI收割 2014 4th IEEE International Conference on Information Science and Technology, Shenzhen, China, 26-28 April 2014 Feng, Quanlong; Gong, Jianhua 收藏  |  浏览/下载：44/0  |  提交时间：2014/12/13 anisotropic diffusion  destriping  remote sensing  wavelet analysis   Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images 期刊论文  OAI收割 ieee transactions on geoscience and remote sensing, 2013, 卷号: 51, 期号: 7, 页码: 4009-4018 作者:  Lu, Xiaoqiang;  Wang, Yulong;  Yuan, Yuan 收藏  |  浏览/下载：32/0  |  提交时间：2015/05/29 Destriping  graph regularizer  hyperspectral image  low-rank representation (LRR)  spectral correlation   An improved destripe noises method for TDI-CCD images (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, August 19, 2011 - August 22, 2011, Jilin, China 作者:  He B. 收藏  |  浏览/下载：21/0  |  提交时间：2013/03/25 The new destriping method using lifting wavelet transform by means of the improved threshold function is presented in this letter. It can overcome the deficiency of the hard and soft threshold function. As compared with the known threshold functions  the quality of the denoised images using the improved threshold function is much better. The results based on several image quality indexes present that the destriped images are not only visually more plausible but also suitable for analysis. Also it is reasonable in computer time and storage space to use the lifting wavelet transform. 2011 IEEE.   An improved approach based on Moment Matching to Destriping for Hyperion data 会议论文  OAI收割 2011 3rd International Conference on Environmental Science and Information Application Technology Esiat 2011, Vol 10, Pt A, Amsterdam Xie, Yi-Song; Wang, Jin-Nian; Shang, Kun 收藏  |  浏览/下载：20/0  |  提交时间：2014/12/07 Hyperion data  Hyperspectral  Destriping  Moment matching  Visual  effect  Quantitative assessment  EO-1 HYPERION  AVIRIS   Destriping of TDI-CCD remote sensing image (EI CONFERENCE) 会议论文  OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者:  He B. 收藏  |  浏览/下载：27/0  |  提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images  a new destriping technique for the improved threshold function using lifting wavelet transform is presented in this letter. It can overcome the shortcoming of the hard threshold function and soft threshold function. The lifting wavelet transform is easily realized. Also it is inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the improved threshold function with some traditional threshold functions both by visual inspection and by appropriate indexes of quality of the denoised images. Evaluations of the results based on several image quality indexes indicate that image quality has been improved after destriping. The destriped images are not only visually more plausible but also suitable for computerized analysis and it did better than the existed ones. 2010 IEEE.   Destriping method using lifting wavelet transform of remote sensing image (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  He B. 收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images  a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm  the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.