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Image Preprocessing is a vital step in the field of image processing forbiometric pattern recognition. This paper studies and reviews various classicaland modern fingerprint image de-noising models. The various model used forde-noising ranges widely from transform matrix using frequency, histogram modelde-noising, de-noising by introducing Gabor filter and its types to enhancefingerprint images.  The output efficiency of various de-noising model proposed earlier iscalculated on the basis of SNR (signal to noise ratio) and MSE (mean squareerror rate). Our simulated experimental results indicates that incorporatingthe de-noising model based on Gabor filter inside domain of wavelet ranges withcomposite method only betters MSE (Mean Square Error). Improved MSE withoutsignificant improvement in SNR improves the fingerprint images only by a littlemargin which is non-optimal in nature. Thus the objective of this researchpaper is to build an optimal de-noising model for fingerprint images so thatits usage in biometric authentication can be more robust in nature.

A Literature Survey of various Fingerprint De-noising Techniques to justify the need of a new De-noising model based upon Pixel Component Analysis