Image Denoising Using Curvelet Transform Using Log Gabor Filter
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 4
Abstract
— In this paper we propose a new method to reduce noise in digital image. Images corrupted by Gaussian Noise is still a classical problem. To reduce the noise or to improve the quality of image we have used two parameters i.e. quantitative and qualitative. For quantity we will compare peak signal to noise ratio (PSNR). Higher the PSNR better the quality of the image. For quality we compare Visual effect of image. Image denoising is basic work for image processing, analysis and computer vision. The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.In this paper we proposed a Curvelet Transformation based image denoising, which is combined with Gabour filter in place of the low pass filtering in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise. Experimental results show that our proposed method gives comparatively higher peak signal to noise ratio (PSNR) value, are much more efficient and also have less visual artifacts compared to other existing methods.
Authors and Affiliations
Assistant Professor Vishal Garg , Scholar Nisha Raheja
Keywords
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- EP ID EP141373
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