A review on natural image denoising using independent component analysis (ica) technique

Denoising of natural images is the fundamental and challenging research problem of Image processing. This problem appears to be very simple however that is not so when considered under practical situations, where the type of noise, amount of noise and the type of images all are variable parameters, and the single algorithm or approach can never be sufficient to achieve satisfactory results. Fourier transform method is localized in frequency domain where the Wavelet transform method is localized in both frequency and 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 data adaptiveness property. The noise is considered as Gaussian random variable and the image data is considered as non-Gaussian random variable. Specifically the Natural images are considered for research as they provide the basic knowledge for understanding and modeling of human vision system and development of computer vision systems. This paper reviews significant existing denoising methods based on Independent Component Analysis and concludes with the tabular Summary of denoising methods and their salient features / applications.