• Pragati Sahu
• Shailesh Khaparkar
• Pankaj Sahu
• Ritesh Beohar

: Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. In this work a new image denoising algorithm by using a non-parametric statistical model of wavelet coefficients, in which 3 thresholding levels are defined for improvement of PSNR and BER performance.

AWGN, Image Denoising, Noise, Filtering, DWT, threshold, PSNR, BER.

"Image denoising by Adaptive Wavelet Thresholding with Generalized Gaussian distribution using Modified BayesShrink Thresholding Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 3, page no.144-148, March-2017, Available :http://www.jetir.org/papers/JETIR1703031.pdf

"Image denoising by Adaptive Wavelet Thresholding with Generalized Gaussian distribution using Modified BayesShrink Thresholding Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 3, page no. pp144-148, March-2017, Available at : http://www.jetir.org/papers/JETIR1703031.pdf