TY - GEN
T1 - A new algorithm of image denoising based on stationary wavelet multi-scale adaptive threshold
AU - Yang, Jianhua
AU - Feng, Rong
AU - Deng, Wei
PY - 2011
Y1 - 2011
N2 - In order to denoise while preserve image details better leading to a satisfactory result, so that it can be analyzed and applied subsequently, in view of advantages of well time-frequency characteristic, multi-resolution and decorrelation of stationary wavelet transform, this paper proposed a new algorithm of image denoising based on multi-scale and adaptive thresholding. In this algorithm: Firstly, use stationary wavelet to transform image. Then determine adaptive threshold of every decomposition progression according to the ratio of noise variance and wavelet coefficient variance. Secondly, process the wavelet coefficient matrice with threshold neighborhood sliding window and adaptively optimization wavelet coefficient processing window. Lastly, obtain resumed image through inverse transform. The experimental results show that, the algorithm can not only obtain clearer image edges but also denoise effectively compared to existing methods.
AB - In order to denoise while preserve image details better leading to a satisfactory result, so that it can be analyzed and applied subsequently, in view of advantages of well time-frequency characteristic, multi-resolution and decorrelation of stationary wavelet transform, this paper proposed a new algorithm of image denoising based on multi-scale and adaptive thresholding. In this algorithm: Firstly, use stationary wavelet to transform image. Then determine adaptive threshold of every decomposition progression according to the ratio of noise variance and wavelet coefficient variance. Secondly, process the wavelet coefficient matrice with threshold neighborhood sliding window and adaptively optimization wavelet coefficient processing window. Lastly, obtain resumed image through inverse transform. The experimental results show that, the algorithm can not only obtain clearer image edges but also denoise effectively compared to existing methods.
KW - adaptive thresholdt
KW - adaptively optimize window
KW - image denoising
KW - stationary wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=80053411293&partnerID=8YFLogxK
U2 - 10.1109/EMEIT.2011.6024042
DO - 10.1109/EMEIT.2011.6024042
M3 - 会议稿件
AN - SCOPUS:80053411293
SN - 9781612840857
T3 - Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011
SP - 4550
EP - 4553
BT - Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011
T2 - 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011
Y2 - 12 August 2011 through 14 August 2011
ER -