Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Signal Processing, Volume 93, Issue 5, May 2013, Pages 1172-1191
Gang Xiong, Tian-Huai Ding
Laboratory of Oil’s Rapid Detection, Logistical Engineering University, Chongqing 401311, China
State Key Laboratory of Precision Measuring Technology and Instruments, Room 3506 of Building 9003, Tsinghua University, Beijing 100084, China
Department of Precision Instruments and Mechanology, Room 3506 of Building 9003, Tsinghua University, Beijing 100084, China
Abstract
While denoising a signal, the existing filtering methods are beset with the contradiction between signal’s noise removal and feature preservation; in face of this denoising contradiction, there is no effective solution by now and they have to make a trade-off between these two contradictory aspects. With the purpose to further alleviate this contradiction progressively, but not to make a trade-off, here we established a universal denoising paradigm named as attribute distance weighted average (ADWA) from the perspective of signal’s attribute analysis. ADWA not only breaks the existing methods’ limitations on the number and kinds of signal’s attributes, and has a flexible expandability to include any attributes, but also includes the ideas and takes the advantages of many existing filtering methods. Both theoretical analysis and experimental results show that, for any noisy signal, under the condition of a good performance of noise removal, ADWA’s ability in signal’s feature preservation can be progressively improved by introducing new significant attributes; this makes ADWA can obtain an enough satisfactory performance in both noise removal and feature preservation. Therefore, ADWA provides an effective and promising way to cope with the denoising contradiction.
Additional Information:
a). Any signal and noise both have infinite attributes.
b). Many existing denoising methods only focus on signal’s some individual attribute, but ADWA concerns many attributes simultaneously.
c). ADWA can further improve the denoising performance progressively by introducing new attributes.
d). ADWA has great inclusiveness of other denoising methods.
e). For the denoising performance of ADWA, there is no best, only better.
