IMAGE-DENOISING-USING-DUAL-TREE-STATISTICAL-MODELS-FOR-COMPLEX-WAVELET-TRANSFORM-COEFFICIENT-MAGNITUDE
Rs4,500.00
10000 in stock
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Analysing images, to estimate the underlying parameters that lead to their formation, is fundamentally an inverse problem. Since the observed image alone is usually not enough to uniquely determine these parameters, statistical models are frequently used to choose a likely solution from amongst those that are consistent with this observation. Deblurring is the process of removing blurring artifacts from images, such as blur caused by defocus aberration or motion blur. Wavelet shrinkage is a standard technique for denoising natural images. Originally proposed for univariate shrinkage in the Discrete Wavelet Transform (DWT) domain, it has since been optimise through the exploitation of translationally invariant wavelet de-compositions such as the Dual-Tree Complex Wavelet Transform (DT-CWT) alongside bivariate analysis techniques that condition the shrinkage on spatially related coefficients across neighbouring scales. These more recent techniques have denoised the real and Imaginary components of the DT-CWT coefficients separately. Processing real and imaginary components separately has been found to lead to an increase in the phase noise of the transform which in turn affects denoising performance. On this basis, the work presented in this paper offers improved denoising performance through modelling the bivariate distribution of the coefficient magnitudes. The results were compared to the current state of the art non-local means denoising technique BM3D, showing clear subjective improvements, through the retention of high frequency structural and textural information. The paper also compares objective measures, using both PSNR and the more perceptually valid structural similarity measure (SSIM). Whereas PSNR results were slightly below those for BM3D, those for SSIM showed closer correlation with subjective assessment, indicating improvements over BM3D for most noise levels on the images tested.
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