High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution
High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution
复制标题
通过非均匀采样和基于超分辨率的自回归建模实现高质量脉冲噪声消除
DOI：
10.1049/iet-ipr.2015.0216
复制
发表时间：
2016-04
影响因子：
2.3
通讯作者：
Guangming Shi
中科院分区：
文献类型：
--
作者：
Xiaotian Wang;Guangming Shi
图像脉冲噪声去除的挑战是利用随机位置的剩余像素恢复损坏像素的空间细节。现有的方法大多使用局部窗口内所有未受污染的像素，通过统计方法估计中心噪声像素。这些方法有两个缺陷。首先，所有噪声像素被视为独立的个体，由它们的邻居逐个估计，忽略它们真实值之间的相关性。其次，图像结构作为一种自然特征通常被忽略。本研究提出了一种新的去噪框架，该框架通过非均匀采样和基于监督分段自回归的超分辨率建模共同恢复所有噪声像素。该方法将图像结构特征作为一项重要约束条件，通过求解一个精心设计的优化问题对噪声像素进行分组联合估计。另一个贡献是，分段自回归模型不是简单地采用，而是精心设计，以便所有无噪声像素可以用来监督模型训练和优化问题的解决，以获得更高的精度。实验结果表明，该方法在较大的噪声密度范围（10 ~ 90%）内具有良好的去噪性能。
The challenge of image impulse noise removal is to restore spatial details from damaged pixels using remaining ones in random locations. Most existing methods use all uncontaminated pixels within a local window to estimate the centred noisy one via a statistic way. These kinds of methods have two defects. First, all noisy pixels are treated as independent individuals and estimated by their neighbours one by one, with the correlation between their true values ignored. Second, the image structure as a natural feature is usually ignored. This study proposes a new denoising framework, in which all noisy pixels are jointly restored via non-uniform sampling and supervised piecewise autoregressive modelling based super-resolution. In this method, the noisy pixels are jointly estimated in groups through solving a well-designed optimisation problem, in which image structure feature is considered as an important constraint. Another contribution is that piecewise autoregressive model is not simply adopted but carefully designed so that all noise-free pixels can be used to supervise the model training and optimisation problem solving for higher accuracy. The experimental results demonstrate that the proposed method exhibits good denoising performance in a large noise density range (10–90%).
登录
查看更多内容
DOI：
10.1007/s11760-013-0517-3
发表时间：
2013-06
期刊：
Signal, Image and Video Processing
影响因子：
--
作者：
Veerakumar Thangaraj;Esakkirajan Sankaralingam;I. Vennila
通讯作者：
Veerakumar Thangaraj;Esakkirajan Sankaralingam;I. Vennila
DOI：
10.1109/icip.2007.4379987
发表时间：
2007-11
期刊：
2007 IEEE International Conference on Image Processing
影响因子：
--
作者：
Xiangjun Zhang;Xiaolin Wu
通讯作者：
Xiangjun Zhang;Xiaolin Wu
影响因子：
10.6
作者：
Chan, RH;Ho, CW;Nikolova, M
通讯作者：
Nikolova, M
影响因子：
10.6
作者：
Zhe Zhou
通讯作者：
Zhe Zhou
影响因子：
10.6
作者：
I. Jafar;R. A. Namneh;Khalid A. Darabkh
通讯作者：
I. Jafar;R. A. Namneh;Khalid A. Darabkh