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Articles by R. Sukanesh
Total Records ( 3 ) for R. Sukanesh
  S. Sudha , G.R. Suresh and R. Sukanesh
  This study proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding based on the Generalized Gaussian Distribution (GGD) widely used in image processing applications. The proposed threshold is simple and it is adaptive to each sub band because it depends on data-driven estimates of the parameters. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, variance. Our method describes a new method for suppression of noise in image by fusing the wavelet denoising technique with optimized thresholding function improving the denoised results significantly. Simulated noise images are used to evaluate the denoising performance of proposed algorithm along with another wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the PSNR and the prevented edge information in most cases. We have compared this with various denoising methods like wiener filter, VisuShrink and BayesShrink.
  G.R. Suresh , S. Sudha and R. Sukanesh
  Region-based coding is an important feature provided in today’s image coding schemes including SPIHT and JPEG2000 as it allows different regions of interest in an image to be encoded at different bit rates and hence at different qualities rather than encoding the entire image with a single quality constraint. This study proposes an algorithm for three-dimensional region-based coding of volumetric medical datasets like MRI sequence. A 3D SA-DWT is used to decompose the data with multiple, arbitrarily shaped regions to obtain the representation of the regions in the transform domain. Then, a modified 3D SPIHT coding algorithm based on an unbalanced 3 structure is adopted in 3D region-based coding. This coding scheme offers good rate-distortion performance with additional features such as distortion scalability and flexibility in precise rate control. Experimental results show that the proposed algorithm outperforms the other coding schemes based on SPIHT algorithm in terms of R-D performance and quality of the image.
  T. Bala Ganesan and R. Sukanesh
  Segmentation of images is the key step in image analysis. This study deals with Brain Magnetic Resonance Image segmentation. Any medical image of human being consists of distinct regions. These regions could be represented by Wavelet Coefficients. Classification of these features may be performed using Fuzzy Clustering Method (FCM-Fuzzy C-Means Algorithm). Edge detection technique is used to detect the edges of the given images. Silhouette method is used to find the strength of clusters. Finally, the different regions of the images are demarcated and color coded.
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