Infrared Image Denoising Based on Classified Wavelet Coefficients Using Zerotree Structure
Ying Cao-qian · Guangdian gongcheng · 2012
Infrared image is vulnerable to noise pollution. In order to improve the quality of the infrared image, a denoising algorithm based on classified wavelet coefficients using zerotree structure was proposed. First, the wavelet coefficients were classified via adaptive threshold by expressing the inter-scale dependencies using zerotree structure. Then, various prior distribution models were adopted to represent various statistic characteristics of different class's coefficients. Finally, infrared image denoising was implemented by Bayes estimation. Experimental results show that the performance of the proposed algorithm is superior to the traditional algorithms in terms of the Peak Signal to Noise Ratio (PSNR). As for visual quality, the proposed algorithm could reduce the noise effectively and retain more details simultaneously. Therefore, it can meet the general demand of denoising for infrared image.