Title :
Performance evaluation and comparison of modified denoising method and the local adaptive wavelet image denoising method
Author :
Parmar, J.M. ; Patil, Shrikant A.
Author_Institution :
Dept. of Electron. & Telecommun., R.C.P.I.T., Shirpur, India
Abstract :
Removal of noise is an important step in the image restoration process, but denoising of image remains a challenging problem in recent research associate with image processing. Denoising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. In this paper, to find out denoised image the modified denoising method and the local adaptive wavelet image denoising method can be used. The noisy image is denoised by modified denoising method which is based on wavelet domain and spatial domain and the local adaptive wavelet image denoising method which is based on wavelet domain. In this paper, we have evaluated and compared performances of modified denoising method and the local adaptive wavelet image denoising method. These methods are compared with other based on PSNR (Peak signal to noise ratio) between original image and noisy image and PSNR between original image and denoised image. Simulation and experiment results for an image demonstrate that RMSE of the local adaptive wavelet image denoising method is least as compare to modified denoising method and the PSNR of the local adaptive wavelet image denoising method is high than other method. Therefore, the image after denoising has a better visual effect. In this paper, these two methods are implemented by using MATLAB for denoising of image.
Keywords :
image denoising; image restoration; mean square error methods; wavelet transforms; Matlab; PSNR; RMSE; acquisition process; corrupted image; image edges; image features; image processing; image restoration process; local adaptive wavelet image denoising method; modified denoising method; noise removal; noisy image denoising; original image; peak signal-to-noise ratio; performance evaluation; spatial domain; storage-and-retrieval process; transmission-and-reception process; visual effect; wavelet domain; Image denoising; PSNR; Wavelet domain; Wavelet transforms; Wiener filters; Adaptive wiener filter; PSNR; image denoising; wavelet transform;
Conference_Titel :
Intelligent Systems and Signal Processing (ISSP), 2013 International Conference on
Conference_Location :
Gujarat
Print_ISBN :
978-1-4799-0316-0
DOI :
10.1109/ISSP.2013.6526883