Informatics Publishing Limited

Seema S. Patil ¹, A. B. Patil ², Manjusha N. Chavan ³

Affiliations
1 Dept. of E & TC Engg., Rajarambapu Institute of Technology, Sakharale, Rajaram Nagar, Dist. Sangli, Maharashtra-415414, IN
2 Dept. of Electrical Engg., Walchand College of Engineering, Sangli, Maharashtra-416416, IN
3 Dept. of E & TC Engg., Rajarambapu Institute of Technology, Sakharale, Rajaramnagar, Dist. Sangli, Maharashtra-415414, IN

Abstract

The search for efficient image denoising methods still is a valid challenge, at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image models corresponds to the algorithm assumptions, but fails in general and creates artifacts or remove image fine structures. The primary goal of noise reduction is to remove the noise without losing much detail contained in an image. To achieve this goal, we make use of a mathematical function known as the wavelet transform to localize an image into different frequency components or useful subbands and effectively reduce the noise in the subbands according to the local statistics within the bands.

Keywords

Image Denoising, Wavelet Transform, Hard Thresholding, Soft Thresholding.

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Manjusha N. Chavan ¹, Mahesh S. Chavan ¹, Seema S. Patil ¹

Affiliations
1 Department of Electronics and Telecommunication, Rajarambapu Institute of Technology, Rajaramnagar, Sakharale (MS), IN

Abstract

Speech denoising is very essential for many applications in this era as most of the communication is wireless which is often prone to noise. Noise is unwanted signal and interference of which is unavoidable in any kind of communication systems. Due to presence of noise, speech signal loses its intelligibility and it is very difficult to process such corrupted signal. Main aim of denoising algorithm is to eliminate noise from noisy speech signal without losing its intelligibility. But various algorithms implemented so far by the researchers are not that much effective as they either loose data or generate artificial noise called musical noise. Denoising algorithms experimented so far are not effective as they try to remove noise with same threshold for different frequency bands. Wavelet transform which is recent tool is suitable for non stationary signal such as speech as it facilitates use of different window size and threshold for different frequency bands. In this paper we have implemented wavelet transform for speech denoising. Here focus is on eliminating additive white gaussian noise from speech signal. Denoising is achieved by implementing Symlet with hard and soft threshold methods where threshold selection is done using rigorous sure technique. Results obtained are compared for SNR (Signal to noise ratio) and MSE (Mean square error). Results show that Symlet is successfully implemented for   denoising the speech signal.

Keywords

Additive White Gaussian Noise, Denoising, Symlet Wavelet, Thresholding.

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