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Image de-noising is a vital concern in image processing. Out of different available method wavelet thresolding method is one of the important approaches for image de-noises. In this paper we propose an adaptive method of image de-noising in the wavelet sub-band domain assuming the images to be contaminated with noise based on threshold estimation for each sub-band. Under this framework the proposed technique estimates the threshold level by apply sub-band of each decomposition level. This paper entails the development of a new MATLAB function based on our algorithm. The experimental evaluation of our proposition reveals that our method removes noise more effectively than the in-built function provided by MATLAB .One of its applications for Fingerprint de-noise due to importance of fingerprint for day-to-day life especially in computer security purposes. Fingerprint acts as a vital role for user authentication as it is unique and not duplicated. Unfortunately allusion Fingerprints may get corrupted and polluted with noise during possession, transmission or retrieval from storage media. Many image processing algorithms such as pattern recognition need a clean fingerprint image to work effectively which in turn needs effective ways of de-noising such images. We apply our proposed algorithm and compare other traditional algorithms for different noises.

Improvising MSN and PSNR for Finger-Print Image noised by GAUSSIAN and SALT & PEPPER