• T. Venkata Ramana
• Dr. S. A. K. Jilani

The image acquired from a sensor is always degraded by some form of noise. The noise can be estimated and removed by the process of denoising the image. Recently, Shape Adaptive methods of denoising have gained popularity. The Shape Adaptive Discrete Wavelet Transform (SADWT) transforms and codes the arbitrarily-shaped regions obtained by a segmentation of the image. The arbitrary shapes preserve the edges, articrafts and produce a high quality image. The features of the SA-DWT’s include the number of coefficients after SA-DWT’s is identical to the number of pixels in the original arbitrarily shaped visual object and the spatial correlation, locality properties of wavelet transforms, and self-similarity across subbands are well preserved. For a rectangular region, the SA-DWT becomes identical to the conventional wavelet transforms. In this paper, the SADWT is evaluated for various images by comparing in terms of Peak Signal to Noise Ratio and Improvement in Signal to Noise Ratio.

Denoising, ISNR, PSNR, Shape Adaptive Methods.

"Shape Adaptive Discrete Wavelet Transform for Denoising of Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.555-559, February-2019, Available :http://www.jetir.org/papers/JETIR1902473.pdf

"Shape Adaptive Discrete Wavelet Transform for Denoising of Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp555-559, February-2019, Available at : http://www.jetir.org/papers/JETIR1902473.pdf