Image denoising using directional filter banks

Rosiles, Jose Gerardo; Smith, M.J.T.

doi:10.1109/icip.2000.899357

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…We note that, unlike the usual approaches that uses banks of different filters [8], in our method the same filter is implemented to obtain all image components. Briefly, the output of the selective filter F (e) is computed as follows: a rectangular N x N window is centered around the current pixel and four sub-windows are considered (the horizontal line, the vertical line and the two diagonals).…”

Section: Image Decompositionmentioning

confidence: 99%

“…In general, the existing algorithms and methods can be classified into two main categories: the approaches that operate on the input image directly [1,2,3,4] and the methods that first transform the image and modify the components (see for instance [5,6,7,8,9,10]). …”

Section: Introductionmentioning

confidence: 99%

“…The wavelet coefficients are then modified to reduce the noise, and the output image is obtained by the inverse wavelet transform (IWT). Another approach that uses image decomposition was proposed in [8] where a bank of directional filters is used for image decomposition. Curvlet and contourlet transforms were also used for de-noising in [9] and [10] respectively.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Decomposition Scheme for Image De-Noising

Bilcu

Vehviläinen

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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In this paper we introduce a novel decomposition scheme for image de-noising. The processed image is decomposed in several components following a tree structure similar to wavelet decomposition. The main difference rely on the fact that image components are obtained at the output of some adaptive filters while wavelet decomposition uses filters with fixed frequency response. Moreover, in our approach the resolution of the different components is the same. The components are filtered separately and recombined to obtain the output image. The proposed de-noising scheme shows improved performances and simpler implementation compared to other approaches based on wavelet, contourlet and other image transforms.

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Section: Image Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Decomposition Scheme for Image De-Noising

Bilcu

Vehviläinen

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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“…Some of them operate in pixel domain [2][3][4][5][6][7][8] while others first transform the image (using wavelets, DCT, etc) and modify the transform coefficients. [9][10][11][12][13][14][15][16] The common feature of the methods implemented in pixel domain is that the output corresponding to a pixel is computed as a weighted average of the pixels in its neighborhood. Probably one of the most simple algorithm of this kind is the sigma filter 6 that uses unity weights to select the neighboring pixels which are averaged.…”

Section: Introductionmentioning

confidence: 99%

Fast nonlocal means for image denoising

Bilcu

Vehviläinen

2007

Digital Photography III

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Due to camera module miniaturization, the pixel area of the digital sensors decreases which decreases also the signal to noise ratio in the captured images. As a consequence, image de-noising is still an important topic in digital image processing field. In this paper we address the problem of image de-noising using the nonlocal means algorithm. This method has excellent de-noising properties but at the expense of increasing the computational complexity. We propose here a novel approach that provides similar filtering capabilities with much less computational effort and shorter processing time. Our proposed algorithm is compared with the nonlocal means algorithm and with another fast implementation, recently reported, in terms of processing time and noise reduction capability (from both visual impression and mean squared error points of view). The comparative results are presented for artificially degraded images and also for images obtained with a camera phone. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/22/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx SPIE-IS&T/ Vol. 6502 65020R-3 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/22/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…Directional Filter Bank (DFB) is used as a preprocessing step to provide directional discriminating feature spaces. DFB based directional analysis has played a major role in wavelet image denoising [8], fingerprint image enhancement [9]. fingerprint image enhancement in a binary domain [7].…”

Section: Introductionmentioning

confidence: 99%

Cross correlation measure for decision fusion among multiple face classifiers

Khan

Ibrahim²,

Khan³

et al.

Proceedings of the IEEE Symposium on Emerging Technologies, 2005.

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We have developed a classifier decision fusion measure which is used as framework for combining multiple classifier decisions. The combination of different sources of information about a face, in the form of different feature sets and classification methods, provides an opportunity to develop an improved level of verification compared to the use of a single set of classifiers. Recently, the face recognition method based on Principal Component Analysis (PCA) and Directional Filter Bank (DFB) responses is integrated with voting algorithm. We look at the possibility of using cross correlation as a measure to compare the outputs of various classifiers. In our system recognition ability of the PCA is enhanced by providing directional images as inputs and then using the normalized cross correlation as a decision fusion measure. The proposed method fuses the decisions of DFB-PCA on the basis of maximum cross correlation of each directional test image with mean of its respective directional class. The experiment results showed the remarkable recognition rate of 97% in Olivetti data set.

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Image denoising using directional filter banks

Cited by 19 publications

References 13 publications

A Novel Decomposition Scheme for Image De-Noising

A Novel Decomposition Scheme for Image De-Noising

Fast nonlocal means for image denoising

Cross correlation measure for decision fusion among multiple face classifiers

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