A general non-local denoising model using multi-kernel-induced measures
A general non-local denoising model using multi-kernel-induced measures
复制标题
使用多核诱导措施的通用非局部去噪模型
DOI：
10.1016/j.patcog.2013.11.003
复制
发表时间：
2014-04
影响因子：
8
通讯作者：
Qiao, Lishan
中科院分区：
文献类型：
--
作者：
Sun, Zhonggui;Chen, Songcan;Qiao, Lishan
在数字图像采集过程中不可避免地会引入噪声，因此图像去噪一直是一个研究热点。与局部方法对图像局部区域的处理不同，非局部方法利用非局部信息（甚至是整个图像）来完成图像去噪。非局部方法由于其优越的性能，近年来在图像去噪领域受到越来越多的关注。然而，这些方法通常不能很好地处理不同级别和类型的复杂噪声。在机器学习领域，多核方法在处理复杂问题时比单核方法更鲁棒、更有效，因此，我们建立了一个基于多核诱导测度的通用非局部去噪模型（简称GNLMKIM），为我们分析现有滤波器和设计新的滤波器提供了一个平台。在GNLMKIM的帮助下，我们在统一视图下重新解释了两个众所周知的非局部过滤器，并将它们扩展到新的多核过滤器中。综合实验表明，该滤波器在视觉效果和PSNR指标上都取得了令人满意的去噪效果。
Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.
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DOI：
10.1201/9781315220413-4
发表时间：
2018-10
期刊：
Handbook of Neural Network Signal Processing
影响因子：
--
作者：
Klaus-Robert Müller;S. Mika;Koji Tsuda;Koji Schölkopf
通讯作者：
Klaus-Robert Müller;S. Mika;Koji Tsuda;Koji Schölkopf
影响因子：
5.4
作者：
Ben Hamza, A;Krim, H
通讯作者：
Krim, H
DOI：
10.1017/cbo9780511801389.013
发表时间：
2000-03
期刊：
--
影响因子：
--
作者：
N. Cristianini;J. Shawe-Taylor
通讯作者：
N. Cristianini;J. Shawe-Taylor
影响因子：
4
作者：
RUDIN, LI;OSHER, S;FATEMI, E
通讯作者：
FATEMI, E
影响因子：
9
作者：
Junyi Sun;Wenbo Zhao;Jiangwei Xue;Zhiyong Shen;Yi-Dong Shen
通讯作者：
Junyi Sun;Wenbo Zhao;Jiangwei Xue;Zhiyong Shen;Yi-Dong Shen