机构

• 长春光学精密机械与物... [7]

采集方式

• OAI收割 [7]

内容类型

• 期刊论文 [4]
• 会议论文 [3]

发表日期

• 2021 [2]
• 2017 [1]
• 2014 [1]
• 2011 [1]
• 2010 [2]

学科主题

筛选

浏览/检索结果: 共7条，第1-7条 帮助 限定条件 专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 A remote sensing image destriping model based on low-rank and directional sparse constraint 期刊论文  OAI收割 Remote Sensing, 2021, 卷号: 13, 期号: 24 作者:  X. Wu;  H. Qu;  L. Zheng;  T. Gao and Z. Zhang   |  收藏  |  浏览/下载：11/0  |  提交时间：2022/06/13 A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint 期刊论文  OAI收割 Remote Sensing, 2021, 卷号: 13, 期号: 24, 页码: 20 作者:  X. B. Wu;  H. S. Qu;  L. L. Zheng;  T. Gao and Z. Y. Zhang   |  收藏  |  浏览/下载：3/0  |  提交时间：2023/06/14 A destriping method with multi-scale variational model for remote sensing images 期刊论文  OAI收割 Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2017, 卷号: 25, 期号: 1 作者:  Huo, L.-J.;  B. He and D.-B. Zhou   |  收藏  |  浏览/下载：9/0  |  提交时间：2018/06/13 Method of destriping stripe noise of aerial images based on total variation 期刊论文  OAI收割 Guangxue Xuebao/Acta Optica Sinica, 2014, 卷号: 34, 期号: 11 作者:  Jia P.;  Wang D. 收藏  |  浏览/下载：12/0  |  提交时间：2015/04/24 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：18/0  |  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.   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：21/0  |  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：22/0  |  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.