SELECTIVE NEIGHBOURING WAVELET COEFFICIENTS APPROACH FOR IMAGE DENOISING B. Chinna Rao1, M. Madhavi Latha2 1 Department of ECE, RK College of Engineering, Vijayawada, A.P., India, E-mail: [email protected]. 2 Department of ECE, Jntu College of Engineering, Hyderabad, A.P., India, E-mail: [email protected]. The denoising of a natural image corrupted by Gaussian noise is a classical pro...

A model for embedding distortion performance for wavelet based watermarking is presented in this paper. Firstly wavelet based watermarking schemes are generalised into a single common framework. Then a mathematical approach has been made to find the relationship between distortion performance metrics and the watermark embedding parameters. The derived model shows that for wavelet based watermar...

Wavelet based embedded coders such as EZW and SPIHT require full wavelet transform of the image to be buffered for coding. Further, since the transform coefficients are required to be stored in high precision, the buffering requirements are prohibitively high for large images, such as remote sensing images, space borne images, medical images etc. In this paper we investigate, embedded coding of...

Signal classification has many important applications in both of the civilian and military domains. This paper presents a classification of multi-user chirp modulation signals using wavelet higher order statistics features and neural network classifier (NN). In this paper, even higher order moments and cumulants up to order eight from the discrete wavelet transform (DWT) coefficients are propos...

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic for multi-channel image denoising. In particular, intra-scale dependency within wavelet coefficients is modeled using a fuzzy feature. This feature space distinguishes between important coefficients, which belong to image discontinuity and noisy coefficients. Besides this fuzzy feature, we use inter-relation betwee...

Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. But the choice of thresholding function has restricted there wide spread use in image denoising application. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold ...

Deblurring in the presence of noise is a hard problem, especially in ultrasonic and CT images. In this paper, we propose a new method of image deblurring in presence of noise, using symmetric Daubechies complex wavelet transform. The proposed method is based on shrinkage of multilevel Daubechies complex wavelet coefficients, and is adaptive as it uses shrinkage function based on the variance of...

A wavelet−based video coder built on the principles of distributed source coding is described. The encoder employs a syndrome−based encoding strategy for intercoded coefficients while other coefficients are intracoded using an embedded wavelet−based coder designed for the coding of arbitrarily shaped image objects. The decoder uses a reference frame in the domain of a redundant wavelet transfor...

Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the...

We propose a new vein of feature vectors for robust speech recognition that use denoised wavelet coefficients. Greater robustness to unexpected additive noise or spectrum distortions begins with more robust acoustic features. The use of wavelet coefficients is motivated by human acoustic process modelling and by the ability of wavelet coefficients to capture important time and frequency feature...