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For the issue of image denoising, in order to avoid the traditional multi-scale sparse representation methods, which used blocks of different sizes as a base function to represent image, the non-separable wavelets were taken. Their advantages included revealing the multi-scale structure, depicting the texture structure under different scales, and separating different directions and different types...
In this paper, we proposed a novel image denoising method based on clustering using SURE-LET. This method divides the images into several clusters and minimize the Steins unbiased risk estimator (SURE) of each cluster independently, which makes different clusters of pixels were denoised by different threshold functions in the image domain. The proposed method included the traditional SURE-LET as its...
In this paper, we present a novel method to image denoising using non-separable wavelet based SURE-LET approach. Unlike conventional separable wavelet filter banks that limit the ability in capturing directional information, non-separable wavelet filter banks contain the basis elements oriented at a variety of directions which are able to capture different detail information. Using SURE-LET makes...
Images often contain noise due to the capturing devices, environment and even human errors. For the image further processing, compression, fractal and so on, the image denoising is necessary. Wavelet analysis plays a very important role in the image denoising. In this paper, we improve the wavelet thresholding method by using multi-scale thresholds and a new thresholding function. Also, in case of...
Fingerprint enhancement is necessary for low quality images. In this paper, we develop a new method for fingerprint enhancement based on non-separable filter banks. We first decompose the fingerprint image using the non-separable filter banks, which can decompose the fingerprint image efficiently and obtain wavelet coefficients, then modifies the coefficients by applying the adaptive approach to reduce...
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