Wavelet Domain Partition-Based Image Denoising
I. Kim, Kenneth E. Barner · 2006
Wavelet transforms are extensively used for image denoising and compression problems. The sparse property is the major reason for the effectiveness of the nonlinear operation such as thresholding of wavelet-transformed coefficients. Applying the thresholds to all coefficients uniformly, however, produces oversmoothing results on edges, or undersmoothing on uniform regions. Using statistical parameterized models often produces some artifacts because of overparameterizing. Recently, adaptive wavelet thresholding utilizing the correlation of space and adjacent scale has been introduced. This paper introduces a related, but more direct, technique of adaptively processing wavelet coefficients based on partitioning of the coefficient space. In the wavelet domain, the coefficient space is partitioned by a vector quantization method and the mask functions are used to obtain the denoised wavelet coefficients. Simulations show that the proposed technique yields a superior performance compared with current wavelet denoising methods.