• Anshuj jain
• Dr Sanjeev Gupta
• Dr Laxmi singh

Modern technology depends on image or video to understand the things absolutely. These images are collected through the various different sensors. The sensing strategy and capabilities, for multidimensional (MD) signals such as images, videos, multispectral images, brightness fields and biomedical data volumes have become omnipresent. Multidimensional filter banks and the related constructions gives a unified structure and efficient computational. These filters also include configuration, demonstration, and processing of these multidimensional data sets. In this work covers non sub sampled Contourlet transform (NSCT) and its application for noise reduction. This work, an image trous algorithm is used which is based on Nonsubsampled Contourlet Transform (NSCT) coefficient histogram matching. . The one-dimensional transforms has limitation, such as the Fourier and wavelet transforms, we follow a “factual” two dimensional transform that can capture the essential geometrical construction that is the key in visual information to subside the limitations. The most significant thing to explore the geometry in images comes from the discrete nature of the data. We create a discrete-domain multi-resolution and multidirectional expansion using non-separable filter banks. This structure is a consequence in a flexible multi-resolution, local and directional images development using contour segments and hence it is named the Contourlet Transform.The Nonsubsampled Contourlet Transform (NSCT) has been built up and its applications have been studied. Here the construction proposed is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a variable multiscale, multidirectional, and shift invariant image decomposition that can be efficiently implemented. Next to the interior of the proposed scheme is the non separable two-channel nonsubsampled filter bank (NSFB) [1]. Discussion is based on use of the less stringent design condition of the NSFB to design filter that lead to a NSCT with better frequency selectivity and regularity when compared to the Contourlet transform. We evaluate the performance of the NSCT in image Denoising application.. Firstly, in the NSCT domain the original image is decomposing. Secondly, the NSCT coefficient histograms of the original image in corresponding sub-band are adaptively mapped to those of the reference image via histogram matching after threshold Denoising. Finally, the superior image is reconstructed from the modified coefficients via inverse NSCT. Investigational results express that the proposed adaptive algorithm efficiently improves slight features while suppressing noise compared with presented algorithms.

NSCT, NSFB, filter banks, Nonsubsample, Contourlet transform

"Non sub sampled Contourlet Transform Application in image Denoising", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.437-443, June 2019, Available :http://www.jetir.org/papers/JETIR1908531.pdf

"Non sub sampled Contourlet Transform Application in image Denoising", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp437-443, June 2019, Available at : http://www.jetir.org/papers/JETIR1908531.pdf