TY - JOUR
T1 - Pre-selection based non-local passive millimeter wave image denoising algorithm
AU - Yu, Wang Yang
AU - Chen, Xiang Guang
AU - Wu, Lei
N1 - Publisher Copyright:
© 2015, Beijing Institute of Technology. All right reserved.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Given that the low resolution and poor texture information of passive millimeter wave images, a pre-selection based image de-noising algorithm (PBNL) was proposed in this paper. The local regional characteristics of the image were obtained by using the singular value decomposition (SVD) of image gradient information, then the image was divided into different categories, and relevant algorithms were adopted. Experimental results show that, in contrast to the non local means (NL-Means) algorithm, the computational time complexity of the method proposed in this paper is significantly reduced and peak signal-to-noise ratio (PSNR) is superior to current state-of-art denoising algorithms, such as BM3D, anisotropic denoising algorithm, and it can get a better recognition results visually.
AB - Given that the low resolution and poor texture information of passive millimeter wave images, a pre-selection based image de-noising algorithm (PBNL) was proposed in this paper. The local regional characteristics of the image were obtained by using the singular value decomposition (SVD) of image gradient information, then the image was divided into different categories, and relevant algorithms were adopted. Experimental results show that, in contrast to the non local means (NL-Means) algorithm, the computational time complexity of the method proposed in this paper is significantly reduced and peak signal-to-noise ratio (PSNR) is superior to current state-of-art denoising algorithms, such as BM3D, anisotropic denoising algorithm, and it can get a better recognition results visually.
KW - Non-local means
KW - Passive millimeter wave
KW - Pre-selection
KW - Singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=84955253236&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2015.12.017
DO - 10.15918/j.tbit1001-0645.2015.12.017
M3 - Article
AN - SCOPUS:84955253236
SN - 1001-0645
VL - 35
SP - 1303
EP - 1307
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 12
ER -