Adaptive Image De-Noising Model Based on Multi-Wavelet with Emphasis on Pre-Processing?

The field of signal or image processing naturally deals with the image de-noising. The image may be corrupted by a noise and/or poor illumination and/or high temperature, and/or transmission. The ability of capturing the energy of signal provides us the better solution towards de-noising of a natural images corrupted by Gaussian noise using multi-wavelet techniques. Multi-wavelet can gratify with symmetry and asymmetry which are very imperative characteristics in signal processing. The image will be highly de-noised if and only if the degree of the noise is lesser. Normally, its energy is dispersed over low frequency band while both its noise and details are dispersed over high frequency band. Corresponding hard threshold used in various scale high frequency sub-bands. In this paper proposed to indicate the aptness of various wavelets and multi-wavelet based and a size of different neighborhood on the performance of image de-noising algorithm in terms of PSNR value. Finally it compares wavelet and multi-wavelet techniques and produces best de-noised image using multi-wavelet technique based on the performance of image de-noising algorithm in terms of PSNR Values.