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	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; Li, Hua; Liu, Qinhuo 收藏 \| 浏览/下载：28/0 \| 提交时间：2016/04/20 Striping noise Destriping Optical imaging instrument Detector response function Improved histogram matching
	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：29/0 \| 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：30/0 \| 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.
	NEW THOUGHTS FOR ONBOARD COMPRESSION OF SATELLITE IMAGES 会议论文 OAI收割 2009 Ieee International Geoscience and Remote Sensing Symposium, Vols 1-5, New York Gao, Lianru; Ran, Qiong; Zhang, Bing; Chi, Yaobin; Wang, Zhiyong 收藏 \| 浏览/下载：38/0 \| 提交时间：2014/12/07 Image compression Striping noise Beijing-1 small satellite
	Study of removing striping noise in CCD image (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 Striping noise is the common system noise during formation of image using linear array CCD and has the character of periodicity directivity and banding distributing. It can be caused by errors in internal calibration devices or by slight gain/offset differences among the elements that conform the array of detectors. Striping noise covers up useful information in CCD image and brings adverse effect to image interpretation. On the basis of analyzing wavelet decomposed coefficient the regularities of distribution about striping noise in wavelet coefficient is found thereby the method of wavelet threshold selection which is suitable to striping noise distribution is put forward. According to Donoho's method about denoising using wavelet the image including striping noise is processed. Comparing the power spectrum of processed image with the one of original image polluted by striping noise in frequency field we find pulse brought by striping noise is removed and the goal which reserves image details and reduces stripes is achieved.