A Novel Color Image Denoising Technique Using Window Based Soft Fuzzy Filter

The image corrupted by different kinds of noises is a frequently encountered problem in image acquisition and transmission. The noise comes from noisy channel transmission errors. The impulse noise (or salt and pepper noise) is caused by sharp, unexpected disturbances in the image signal; its appearance is randomly scattered white or black (or both) pixels over the image. Gaussian noise is an idealized form of white noise, which is caused by some random fluctuations in the signal. Speckle noise (or more simply just speckle) can be modelled by random values multiplied by pixel values; hence it is also called multiplicative noise. This work presents a novel technique for edge preserved color image denoising using window based soft fuzzy filter based on asymmetrical triangular membership function. However lots of techniques like median, mean and average filters are available for gray image denoising, but most of the time it is found that all these filters are capable to provide good noise removal for some specific type of noise, but cant able to preserve the edges of the images ie the output images were greatly suffers from the blurring effect. So to address this problem the proposed technique not only concentrates on efficient noise removal as well as preservation of image edges. To handle this problem fuzzy logic based soft technique is proposed, because of imprecise and vague situations handling capability of fuzzy based techniques. To illustrate the proposed method, experiments have been performed on color test image like Lena and results are compared with other popular image denoising methods. For the comparative analysis of the proposed work a comparison between conventional filters and proposed filter has been also presented in the thesis on the basis of three important parameters Mean square error (MSE), Peak signal to noise ratio (PSNR) and Edge Preservation index (EPI). The obtained results show that the proposed method has very good performance with desirable improvement in the PSNR and MSE of the image.