Recently, a logarithmicimageprocessing model called symmetric logarithmic image processing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-L...
详细信息
ISBN:
(纸本)9781479983391
Recently, a logarithmicimageprocessing model called symmetric logarithmic image processing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-LIP model is an extension of the logarithmicimageprocessing (LIP) model. The motivation of this work is to implement classical wavelet applications in the S-LIP framework. The underlying idea is to take advantage of both the multiscale analysis performed by the wavelet transform and the logarithmicprocessing of the pixels' intensity by the S-LIP model. The S-LIP wavelet transform is introduced and applied to automatic denoising in order to highlight its intrinsic characteristics. As an illustration, signal-to-Noise Ratios for both the linear wavelet transform and S-LIP wavelet transform are calculated for different levels of Gaussian, Poisson, Speckle and salt-and-pepper noises.
Recently, a logarithmicimageprocessing model called symmetric logarithmic image processing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-L...
详细信息
ISBN:
(纸本)9781479983407
Recently, a logarithmicimageprocessing model called symmetric logarithmic image processing (S-LIP) has been investigated in the framework of the multiresolution analysis (MRA) performed by wavelet transform. The S-LIP model is an extension of the logarithmicimageprocessing (LIP) model. The motivation of this work is to implement classical wavelet applications in the S-LIP framework. The underlying idea is to take advantage of both the multiscale analysis performed by the wavelet transform and the logarithmicprocessing of the pixels' intensity by the S-LIP model. The S-LIP wavelet transform is introduced and applied to automatic denoising in order to highlight its intrinsic characteristics. As an illustration, signal-to-Noise Ratios for both the linear wavelet transform and S-LIP wavelet transform are calculated for different levels of Gaussian, Poisson, Speckle and salt-and-pepper noises.
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