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Image Denoising Using Framelet Transform | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 28, Issue 13, 2010, Pages 2530-2550 PDF (554.49 K) | ||
| DOI: 10.30684/etj.28.13.3 | ||
| Authors | ||
| Ali K. Nahar; Hadeel N. Abduallah | ||
| Abstract | ||
| In many of the digital image processing applications, observed image is modeled to be corrupted by different types of noise that result in a noisy version. Hence image denoising is an important problem that aims to find an estimate version from noisy image that is as close to the original image as possible. In this paper, introduces firstly was applied method of computing one and twodimensional framelet transform .The applying method reduces heavily processing time for decomposition of image keeping or overcoming the quality of reconstructed images. In addition, it cuts heavily the memory demands .Also, the inverse procedures of all the above transform for multi- dimensional cases verified. Secondly, many techniques are proposed for denoising of gray scale and color image. A new threshold method is proposed and compared with the other thresholding methods. For hard thresholding, PSNR gives (13.548) value while the PSNR was increased in the proposed soft thresholding, it gives (14.1734) PSNR value when the noise variance is (20). Some of the above denoising schemes are tested on Peppers image to find its effect on denoising application. The noisy version with SNR is equal to (11.9373 dB), the denoising image using WT with SNR is equal to (17.4661 dB), the denoising image using SWT with SNR is equal to (18.1459 dB), the denoising image using WPT with SNR is equal to (19.3640 dB), the denoising image using FT with SNR is equal to (21.9138 dB). Finally the denoising image for color image using FT with SNR is equal to (27.3443 dB). | ||
| Keywords | ||
| Fast Computing; Framelet Transform; Image Denoising; Filter Banks; Inverse Framelet Transform | ||
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