Noises present in signals are difficult to recover using the traditional methods. Now wavelet transform is used for denoising techniques. The thresholding both hard and soft are used in wavelet transform. But around discontinuities it creates Gibbs phenomenon. This is the main drawback of using wavelets. Here traditional method of total variation minimization is used for denoising in first step. The Gibbs oscillations are reduced using transformation domain and block matching is used for improvement of SNR. The technique exposes each and every finest details contributed by the grouped set of blocks and also it protects the vital and unique features of every individual block. The blocks are filtered and replaced in their original positions from where they are detached. A technique based on this denoising strategy and its efficient implementation is presented in full detail. The implementation results reveal that the proposed technique achieves a state-of-the-art denoising performance in terms of signal-to-noise ratio.
Image segmentation of threshold based is a useful technique in the preprocessing phase of image processing applications. SomeÂ two dimensional entropy, methods useÂ local propertiesÂ of theÂ image to compute the optimal threshold.Â Yang Xiao et al. simplification on this procedure worked well with the inclusion of spatial correlation features which reduces the time complexity of earlier methods. Seetharama Prasad et al. improvised further in the process of obtaining the varying similarity measure. In this paper type-II fuzzy membership degrees of gray values are employed with probability partition of the image as object and background probabilities. Spatial correlation parameters are used in the computation of entropy criterion function to obtainÂ Â optimal threshold of the image. For low contrast images contrast enhancement is assumed. Experimental results are so encouraging.Â