Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to changing image characteristics. Each wavelet coefficient is modeled as a random variable of a generalized Gaussian distribution with an unknown parameter. Context modeling is used to estimate the parameter for each coefficient, which is then used to adapt th...

• Type: Conference
• Year: 1998
• Where: ICIP
• Authors: S. Grace Chang, Bin Yu, Martin Vetterli
• Keywords: Adaptive Wavelet Thresholding, ICIP 1998, Image Processing, Wavelet Thresholding, Wavelet Thresholding Performance