Patch-based de-noising algorithms and patch manifold smoothing have emerged as efficient de-noising methods. This paper provides a new insight on these methods, such as the Non Local Means [1] or the image graph de-noising [8], by showing its use for filtering a selected pattern. K ̄ eywords: NL-Means, diffusion processes, diffusion geometry, graph filtering, patch manifold.

Image Preprocessing is a vital step in the field of image processing for biometric pattern recognition. This paper studies and reviews various classical and modern fingerprint image de-noising models. The various model used for de-noising ranges widely from transform matrix using frequency, histogram model denoising, de-noising by introducing Gabor filter and its types to enhance fingerprint im...

Over the past decade wavelet transforms have received a lot of attention from researchers in many diierent areas. Both discrete and continuous wavelet transforms have shown great promises in such diverse elds as image compression, image De-noising, signal processing, computer graphics, and pattern recognition, to name a few. Most of the work has been done on scalar wavelets, i.e., wavelets gene...

There exist various image de-noising techniques. Amongst them orthogonal wavelet is preferred one. However, the orthogonal wavelet transform is not better technique as proper clustering of wavelet coefficients is not possible in this technique. So a better image de-noising technique is needed to have a better SNR and greater image information. In this work, image de-noising by linear minimum me...

A critical issue in the image restoration is the problem of de-noising images while keeping the integrity of relevant image information. A large number of image denoising techniques are proposed to remove noise. Mainly these techniques are depends upon the type of noise present in images. So image de-noising still remains a important challenge for researchers because de-noising techniques remov...

A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed...

The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Additive random noise can easily be removed using simple threshold methods. De-noising of natural images corrupted by Gaussian noise using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values. The wavelet de...

During the process of signal testing, it is often exposed to interference and influence of all kinds of noise signal, such as data collection and transmission and so noise may be introduced. So in practical applications, before analysis of the data measured, the need for de-noising processing. The signal denoising is a method for filtering the high frequency noise of the signal and makes the si...

IJSER © 2013 http://www.ijser.org Abstract — This paper presents the Wavelet based Image De-noising. The search for efficient image De-noising methods is still a valid challenge at the crossing of functional analysis and statistics using discrete wavelet transform. De-noising of stationary images corrupted by Gaussian noise using wavelet techniques is very effective because of its ability to ca...

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also pro...