中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共5条，第1-5条

帮助

条数/页：排序方式：
	Single Image-Based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy 期刊论文 OAI收割 FRONTIERS IN NEUROINFORMATICS, 2022, 卷号: 15, 页码: 10 作者: Li, Dong; Wang, Guangyu; Werner, Rene; Xie, Hong; Guan, Ji-Song \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2022/02/24 vignetting correction functional microscopic imaging neural activity image analysis imaging artifacts
	MRI compatible Nb-Ta-Zr alloys used for vascular stents: Optimization for mechanical properties 期刊论文 OAI收割 Journal of the Mechanical Behavior of Biomedical Materials, 2014, 卷号: 32, 页码: 166-176 H. Z. Li; J. Xu 收藏 \| 浏览/下载：51/0 \| 提交时间：2014/07/03 Vascular stent MRI compatibility Tensile properties Niobium Tantalum in-vitro evaluation coronary-arteries strut thickness magnetic-susceptibility imaging artifacts metallic stents elastic recoil porcine model wiktor stent restenosis
	Color filtering method for CFA images based on gradient (EI CONFERENCE) 会议论文 OAI收割 International Conference on Communication Systems and Network Technologies, CSNT 2012, May 11, 2012 - May 13, 2012, Rajkot, Gujrat, India 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 Single-sensor digital cameras capture image by covering the sensor surface with a color filter array(CFA) such that each sensor pixel only samples one of three primary color values three color is R(red) G(green) and B(blue). To render a full-color image need an interpolation process commonly referred to as CFA demosaicking is required to estimate the other two contributions for producing a full-color image. But the noise in imaging sensors not only corrupts the color filter array at the same time introduces artifacts during the color interpolation step and influence quality of images. In order to acquire high quality full-color images adopt a sort of viable and effective interpolation algorithm based on gradient at the time of removing the noise reserve image border and detail information clearly. 2012 IEEE.
	An Efficient Gridding Reconstruction Method for Multishot Non-Cartesian Imaging With Correction of Off-Resonance Artifacts 期刊论文 OAI收割 MAGNETIC RESONANCE IN MEDICINE, 2010, 卷号: 63, 期号: 6, 页码: 1691-1697 作者: Meng, Yuguang; Lei, Hao 收藏 \| 浏览/下载：22/0 \| 提交时间：2015/06/25 non-Cartesian imaging spiral imaging gridding reconstruction off-resonance artifacts
	Level 0 and level 1 data processing for a type of hyper-spectral imager (EI CONFERENCE) 会议论文 OAI收割 2009 International Conference on Optical Instruments and Technology, OIT 2009, October 19, 2009 - October 21, 2009, Shanghai, China Li X.; Yan C. 收藏 \| 浏览/下载：74/0 \| 提交时间：2013/03/25 Hyper-spectral imaging (HSI) is a kind of optical remote sensor that can simultaneously obtain spatial and spectral information of ground targets. We are now designing a data processing system for a type of space-borne push-broom HSI then it performs radiometric and spectral calibration based on the ground calibration results and onboard calibration collection. The detailed algorithms for bad pixel replacement which has 128 spectral channels covering the spectral range from 400nm to 2500nm. With its large amount of spectral channels radiometric and spectral calibration were presented. After processing the HSI collects large volume of spectral imaging data need to be efficiently and accurately processed and calibrated. In this paper the digital numbers downlinked from the spacecraft can be converted into at-sensor absolute spectral radiance of ground targets the detailed Level 0 and Level 1 data processing steps for the HSI were presented. The Level 0 processing refers to a set of tasks performed on the data downlinked from the spacecraft thus providing accurate quantified spectral imaging data for various applications. 2009 SPIE. including decoding to extract science data separating the science data into files corresponding to different tasks (e.g. ground imaging dark imaging and onboard calibration) checking data integrity and instrument settings data format conversion and Level 0 files creation. The Level 1 processing performs several steps on Level 0 data. Firstly it corrects the image artifacts (mostly the SWIR smear effect) subtracts the dark background and performs the bad pixel replacement according to the prelaunch measurement