Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Variational denoising of partly textured images by spatially varying constraints
IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
IEEE Transactions on Image Processing
Image Denoising Using Derotated Complex Wavelet Coefficients
IEEE Transactions on Image Processing
A Bayesian approach of wavelet based image denoising in a hyperanalytic multi-wavelet context
WSEAS Transactions on Signal Processing
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The SONAR images are perturbed by speckle noise. The use of speckle reduction filters is necessary to optimize the image exploitation procedures. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and textural information of the scene. Shift-invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, for example, the Double Tree Complex Wavelet Transform (DT-CWT) have these useful properties. In this paper, we propose the use of the DT-CWT in association with Maximum A Posteriori (MAP) filters. Such systems carry out different quality denoising in regions with different homogeneity degree. We propose a solution for the reduction of this unwanted effect based on diversity enhancement. The corresponding denoising algorithm is simple and fast. Some simulation results prove the performance obtained.