基于小波包分解的整体变分去噪算法  

Total variation algorithm of image denoising based on wavelet packet decomposition

作　　者：唐昌令[1] 彭国华[1] 

机构地区：[1]西北工业大学理学院数学系,西安710129

出　　处：《计算机工程与应用》2013年第6期156-158,174,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61070233);西北工业大学基础研究基金项目(No.JC200946)

摘　　要：由Rudin等人提出的整体变分(TV)模型被认为是目前最好的图像去噪模型之一。理论表明,TV模型对分块常量的图像去噪效果显著。对于纹理细节丰富的图像,通过引入小波包分解技术,对图像的纹理细节进行多层小波包分解,得到一系列近似分块常量的子图像,用TV模型对子图像分别进行处理,从而图像的纹理细节得到了更好的保留。相对于单独使用TV模型去噪,该方法得到的复原图像峰值信噪比(PSNR)提高了1dB左右。同时由于采用改进的Bregman迭代方案求解TV模型,算法收敛时间得到了极大的减少。The Total Variation（TV） model of Rudin et al. for image denoising is considered as one of the best denoising models. Theory suggests that the TV model denoises well piecewise constant images. For the texture rich image, in this paper, it is decom- posed by multi-wavelet packet into a series of approximate piecewise constant sub-images. Sub-images are processed separately by TV model. Thus, the texture detail of image is preserved better and the Peak Signal to Noise Ratio （PSNR） of denoised image is improved 1 dB, compared with using TV model alone. At the same time, the improved Bregman iteration scheme is adopted to solve TV model. The algorithm convergence time has been greatly decreased.

关 键 词：图像去噪 整体变分(TV)模型 小波包分解 Bregman迭代 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]