06-02-2016, 12:05 PM
matlab code for image denoising using ica
ABSTRACT
Denoising of natural images is the fundamental and challenging research problem of Imageprocessing. This problem appears to be very simple however that is not so when considered under practicalsituations, where the type of noise, amount of noise and the type of images all are variable parameters, andthe single algorithm or approach can never be sufficient to achieve satisfactory results. Fourier transformmethod is localized in frequency domain where the Wavelet transform method is localized in both frequencyand spatial domain but both the above methods are not data adaptive .Independent Component Analysis(ICA) is a higher order statistical tool for the analysis of multidimensional data with inherent dataadaptiveness property. The noise is considered as Gaussian random variable and the image data isconsidered as non-Gaussian random variable. Specifically the Natural images are considered for researchas they provide the basic knowledge for understanding and modeling of human vision system anddevelopment of computer vision systems. This paper reviews significant existing denoising methods basedon Independent Component Analysis and concludes with the tabular Summary of denoising methods andtheir salient features / applications.
ABSTRACT
Denoising of natural images is the fundamental and challenging research problem of Imageprocessing. This problem appears to be very simple however that is not so when considered under practicalsituations, where the type of noise, amount of noise and the type of images all are variable parameters, andthe single algorithm or approach can never be sufficient to achieve satisfactory results. Fourier transformmethod is localized in frequency domain where the Wavelet transform method is localized in both frequencyand spatial domain but both the above methods are not data adaptive .Independent Component Analysis(ICA) is a higher order statistical tool for the analysis of multidimensional data with inherent dataadaptiveness property. The noise is considered as Gaussian random variable and the image data isconsidered as non-Gaussian random variable. Specifically the Natural images are considered for researchas they provide the basic knowledge for understanding and modeling of human vision system anddevelopment of computer vision systems. This paper reviews significant existing denoising methods basedon Independent Component Analysis and concludes with the tabular Summary of denoising methods andtheir salient features / applications.
