Infrared Image Adaptive Denoising Algorithm in Contourlet Transform with Sharp Frequency Localization
Infrared image is usually disturbed by various noises during capturing and transporting, so it should be firstly processed by denoising. A new adaptive denoising algorithm based on a new Contourlet Transform with Sharp Frequency Localization is proposed in this paper. The hierarchical adaptive denoising threshold of new Contourlet coefficient is firstly estimated respectively by each location from different direction, then the noisy image is denoising with soft threshold related to the transform scale and direction. In order to reduce significant amount of aliasing components which are located far away from the desired support because of the new Contourlet Transform, cycle spinning is employed to overcome the lack of translation invariance property and suppress pseudo-Gibbs phenomena around singularities of denoising image. Numerical experiments on infrared noisy image show that the algorithm given by this paper is significantly superior to some other usual arithmetic based on contourlet, which can get better PSNR and visual quality.
sharp frequency localization contourlet transform adaptive denoising cycle spinning PSNR
WANG Fei LIANG Xiao-geng CUI Yan-kai WU Xiao-jun
School of Automation Northwestern Polytechnical University Xian, Shaanxi, China Luoyang Photoelectric Technology Development Center Luoyang, Henan, China Luoyang Institute of Electro-Optical Equiqment Luoyang, Henan, China
国际会议
杭州
英文
44-47
2011-10-28（万方平台首次上网日期，不代表论文的发表时间）