De-noising techniques for terahertz responses of biological samples

Signal processing techniques may be used to improve the speed, resolution and noise robustness of pulsed terahertz (T-ray) imaging systems. Such systems have a wide range of applications and much recent interest has focussed on several promising biomedical fields. There are a number of significant challenges to be overcome before a commercial biomedical terahertz system can be realised. Recent research is focussed on the implementation of a high speed, compact and portable T-ray imaging system. This system will draw heavily on MOEMS technology. One of the major stages in the development of such a system is the design of efficient software algorithms to perform signal recognition and imaging operations in real time.

This paper considers a number of signal processing techniques suitable for de-noising and extracting information from the data obtained in a terahertz pulse imaging system. Two main de-noising techniques are considered. Wavelet de-noising and Wiener deconvolution algorithms are applied to the terahertz responses of biological samples including Spanish Serrano ham and an oak leaf.