Hawwar Yousef, Reza Ali
Mobility Solutions Group, Lucent Technol., Whippany, NJ 07981, USA.
IEEE Trans Image Process. 2002;11(12):1397-404. doi: 10.1109/TIP.2002.804526.
A new image denoising technique in the wavelet transform domain for multiplicative noise is presented. Unlike most existing techniques, this approach does not require prior modeling of either the image or the noise statistics. It uses the variance of the detail wavelet coefficients to decide whether to smooth or to preserve these coefficients. The approach takes advantage of wavelet transform property in generating three detail subimages each providing specific information with certain feature directivity. This allows the ability to combine information provided by different detail subimages to direct the filtering operation. The algorithm uses the hypothesis test based on the F-distribution to decide whether detail wavelet coefficients are due to image related features or they are due to noise. The effectiveness of the proposed technique is tested for orthogonal as well as biorthogonal mother wavelets in order to study the effect of the smoothing process under different wavelet types.
提出了一种在小波变换域中用于乘性噪声的新图像去噪技术。与大多数现有技术不同，该方法不需要对图像或噪声统计进行先验建模。它利用细节小波系数的方差来决定是平滑还是保留这些系数。该方法利用小波变换特性生成三个细节子图像，每个子图像都提供具有特定特征方向性的特定信息。这使得能够组合不同细节子图像提供的信息来指导滤波操作。该算法使用基于F分布的假设检验来决定细节小波系数是由于与图像相关的特征还是由于噪声。为了研究不同小波类型下平滑过程的影响，对正交和双正交母小波测试了所提出技术的有效性。
