School of Computer Science, South China Normal University, Guangzhou 510631, China.
IEEE Trans Image Process. 2013 Mar;22(3):872-83. doi: 10.1109/TIP.2012.2219544. Epub 2012 Sep 18.
Accurate estimation of Gaussian noise level is of fundamental interest in a wide variety of vision and image processing applications as it is critical to the processing techniques that follow. In this paper, a new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images. Two novel aspects of this paper address the major challenges in noise estimation: 1) the use of the tail of singular values for noise estimation to alleviate the influence of the signal on the data basis for the noise estimation process and 2) the addition of known noise to estimate the content-dependent parameter, so that the proposed scheme is adaptive to visual signals, thereby enabling a wider application scope of the proposed scheme. The analysis and experiment results demonstrate that the proposed algorithm can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions, and that is outperforms relevant existing methods.
准确估计高斯噪声水平在各种视觉和图像处理应用中都具有重要意义，因为它对后续处理技术至关重要。本文基于对噪声污染图像奇异值的研究，提出了一种新的有效噪声水平估计方法。本文有两个新颖的方面解决了噪声估计中的主要挑战：1）使用奇异值的尾部进行噪声估计，以减轻信号对噪声估计过程数据基础的影响；2）添加已知噪声来估计与内容相关的参数，从而使所提出的方案适应视觉信号，从而扩大了所提出方案的应用范围。分析和实验结果表明，所提出的算法可以可靠地推断噪声水平，并在广泛的视觉内容和噪声条件下表现出稳健的行为，优于相关的现有方法。
