A NEW APPROACH ON DENOISING FOR 1D, 2D AND 3D IMAGES BASED ON DISCRETE WAVELET TRANSFORMATION AND THRESHOLDING
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Abstract
Image processing is used to improve the quality of the image for better human understanding. Quality of the image is not maintained because of noise. Noise can be removed by various algorithms and it is considered as one of the pre processing step in image processing. In this paper we have proposed a new method for denoising one dimensional, two dimensional and three dimensional images based on discrete wavelet transform.
Wavelet transform is the simplest method to decompose the image signals into sub-bands and to analyse each sub-bands[1]. The key advantage of using discrete wavelet transform is it gives both information such as number of times noise occurred and place where noise is occurred. We have introduced a predictor variable based on discrete wavelet transform which will be efficient in finding the peak signal nose ratio (PSNR). The proposed new algorithm is applied for one dimensional, two dimensional and three dimensional signals, and then the results are compared by using different types of filters. In this paper, different thresholding techniques are used and compared in terms of PSNR in db.
Wavelet transform is the simplest method to decompose the image signals into sub-bands and to analyse each sub-bands[1]. The key advantage of using discrete wavelet transform is it gives both information such as number of times noise occurred and place where noise is occurred. We have introduced a predictor variable based on discrete wavelet transform which will be efficient in finding the peak signal nose ratio (PSNR). The proposed new algorithm is applied for one dimensional, two dimensional and three dimensional signals, and then the results are compared by using different types of filters. In this paper, different thresholding techniques are used and compared in terms of PSNR in db.
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