Research on optimization of NLM denoising algorithm for terahertz imaging
Terahertz transmission imaging has the characteristics of harmless to the human body and high penetration to non-metallic materials.However,the signal-to-noise of imaging pictures is relatively low,so this paper proposes an improved non-local mean filtering algorithm,which first uses structural components to partition the image.The median filtering algorithm is performed for flat areas,and the improved non-local mean filtering algorithm is adopted for non-flat areas,which introduces structural similarity index,image general quality index,and Gaussian weighted distance of feature similarity index as image similarity criteria.In order to explore the effect of the algorithm on terahertz transmission imaging pictures,this paper is divided into single-point,linear array and area scan imaging experiments according to the different types of terahertz detectors,and the imaging results show that there are dead pixels in the linear scan camera that lead to dark stripes in the imaging picture,and the Gaussian-weighted median can be used to eliminate the dark fringes.Single point scanning imaging and area scan imaging have noise due to interference from motion platform movement factors and inconsistent cell response.The results show that compared with the traditional non-local mean filtering algorithm,the improved non-local mean filtering algorithm improves the peak signal-to-noise ratio obtained after terahertz transmission imaging image processing by 1-9 dB,and compares with other traditional denoising algorithms and finds that the peak signal-to-noise ratio of the image after processing the improved non-local mean filtering algorithm also increases by 2-8 dB,and the denoising effect is better.
image denoisingterahertz imagesimage qualitynon-local mean filtering
NETL
NSTL
万方数据
维普
