QQ客服 官方微博 反馈留言

浏览/检索结果: 共6条，第1-6条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Mixed noise removal based on Stokes residual noise removal for division of focal plane polarimetric images 期刊论文 Optics and Lasers in Engineering, 2022, 卷号: 159 作者:  Jiang, Tuochi;  Wen, Desheng;  Song, Zongxi;  Gao, Wei;  Liu, Gang 收藏  |  浏览/下载：45/0  |  提交时间：2022/09/28 DoFP polarization imaging  Laplacian scale mixture  Mixed noise removal   Edge preserving mixed noise removal 期刊论文 MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 卷号: 78, 期号: 12, 页码: 16601-16613 作者:  Guo, Fenghua;  Zhang, Caiming 收藏  |  浏览/下载：15/0  |  提交时间：2019/12/11 Mixed Noise Removal  Edge Preserving  High-frequency Components   Edge preserving mixed noise removal 期刊论文 Multimedia Tools and Applications, 2018 作者:  Guo F.;  Zhang C. 收藏  |  浏览/下载：4/0  |  提交时间：2019/12/11 Edge Preserving  High-frequency Components  Mixed Noise Removal   基于分而治之策略的图像复原与增强算法研究 学位论文 2017, 2016 庄培显 收藏  |  浏览/下载：6/0  |  提交时间：2017/06/20 图像复原  分而治之策略  混合去噪  非盲去模糊  自然性保持增强  image restoration  divide-and-conquer strategy  mixed noise removal  non-blind deconvolution  enhancement of naturalness preservation   Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal 期刊论文 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 卷号: 26, 期号: 7 作者:  Ge, Qi;  Jing, Xiao-Yuan;  Wu, Fei;  Wei, Zhi-Hui;  Xiao, Liang 收藏  |  浏览/下载：9/0  |  提交时间：2019/12/05 Low-rank model  graph nuclear norm regularization  manifold structure  mixed noise removal  weighted singular-value thresholding algorithm   A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Li Y.; Ni H.; Pang W.; Hao Z. 收藏  |  浏览/下载：27/0  |  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.