Blind Noise Estimation Using Signal Statistics in Random Band-Pass Domains

使用随机带通域中的信号统计进行盲噪声估计

• 批准号: 1319800
• 负责人: Siwei Lyu
• 金额: $ 39.97万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319800&HistoricalAwards=false
• 关键词: Blind; Noise; Estimation; Using; Signal

Noise, which corresponds to random variations extrinsic to the signals of interest, is an ubiquitous aspect that affects the performance of many tasks in signal processing. Even with the improving quality and sophistication of the modern acquisition devices, digital signals still carry noise due to many incontrollable factors. This research focuses on the fundamental problem of estimating parameters of the random noise model directly from a noise corrupted signal. As an immediate consequence, the results of this investigation will be applicable in a wide range of fields, including the forensic analysis of digital images, automatic processing of medical images, spectrum sensing in wireless communications and data processing in sensory neuroscience. The technical approach taken in this research exploits the regular statistical properties of the original signals in multiple signal representations and their relationship with the noise parameters. Specifically, we will investigate the use of domains constructed from random band-pass filters that are more effective in revealing ?typical? statistical properties of the signals, especially when compared with deterministic representations such as Fourier, DCT, and wavelet. Concurrently, we will investigate the mathematical relationship between the observed statistics of noisy signal and the noise parameters. Drawing on these theoretical findings, this research is expected to lead to more effective and efficient algorithms for blind noise estimation. More generally, the proposed work will also explore efficient algorithms for blind local noise estimation in the presence of non-stationary noise statistics.

噪声对应于感兴趣的信号的外部随机变化，是一个无处不在的方面，会影响信号处理中许多任务的性能。即使现代采集设备的质量和复杂性提高，由于许多不可控制的因素，数字信号仍然具有噪音。这项研究的重点是直接从噪声损坏的信号中估算随机噪声模型参数的基本问题。直接的结果，这项研究的结果将适用于广泛的领域，包括对数字图像的法医分析，医学图像的自动处理，无线通信中的频谱传感以及感觉神经科学中的数据处理。本研究中采用的技术方法利用了多个信号表示中原始信号的常规统计特性及其与噪声参数的关系。具体而言，我们将研究从更有效揭示的随机带通滤波器中构建的域的使用？信号的统计特性，尤其是与傅立叶，DCT和小波等确定性表示相比。同时，我们将研究观察到的嘈杂信号的统计数据与噪声参数之间的数学关系。利用这些理论发现，这项研究有望导致更有效，更有效的算法以进行盲目估计。更普遍的是，在存在非平稳噪声统计的情况下，建议的工作还将探索有效的局部局部噪声估计算法。