Title :
Traffic sign image denoising with energy-based adaptive finite ridgelet transform
Author :
Liu Yunxia ; Tian Guohui ; Yang Yang ; Zhou Fengyu
Author_Institution :
Sch. of Control Sci. & Eng., Shandong Univ., Jinan, China
Abstract :
Denoising of traffic sign images is an important pre-processing step in intelligent transportation system whose performance greatly affects subsequent manipulations. Based on characteristic analysis of traffic sign images that linear singularities is more informative, this paper introduced the finite ridgelet transform (FRIT) for traffic sign image denoising. An energy based adaptive finite ridgelet transform scheme (EFRIT) is proposed for better presentation of linear singularities. Abundant experimental results carried out on different types of traffic sign images at varying noise levels demonstrate the effectiveness of the proposed algorithm in both terms of PSNR and visual quality.
Keywords :
automated highways; image denoising; image registration; traffic engineering computing; wavelet transforms; EFRIT; PSNR; energy-based adaptive finite ridgelet transform; intelligent transportation system; linear singularities; peak signal-to-noise ratio; traffic sign image analysis; traffic sign image denoising; visual quality; Discrete wavelet transforms; Image denoising; Image reconstruction; Noise reduction; PSNR; Standards; finite ridgelet transform; image denoising; intelligent transportation system; wavelet transform;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese