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Perceptual Soft Thresholding using the Structural Similarity Index
S. S. Channapayya , A. C. Bovik and R. W. Heath Jr.
IEEE International Conference on Image Processing
Abstract
In this paper, we present a novel algorithm for wavelet domain image
denoising using the soft thresholding function. The thresholds
are designed to be locally optimal with respect to the structural similarity
(SSIM) index. The SSIM Index is first expressed in terms
of wavelet transform coefficients of orthogonal wavelet transforms.
The wavelet domain representation of the SSIM Index, along with
the assumption of a Gaussian prior for the wavelet coefficients is
used to formulate the soft thresholding optimization problem. A locally
optimal solution is found using a quasi-Newton approach. This
solution is applied to denoise images in the wavelet domain. The visual
quality of the images denoised using the proposed algorithm is
shown to be higher compared to the MSE-optimal soft thresholding
denoising solution, as measured by the SSIM Index.
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