Wavelet-based adaptive image denoising with edge preservation

Zhan, Charles; Karam, Lina J.

doi:10.1109/icip.2003.1246907

Cited by 15 publications

(13 citation statements)

References 13 publications

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“…Figure 3 shows a block diagram of the proposed texture granularity metric. The input image is first divided into three undecimated wavelet subbands [10] at every level, namely, low-low (LL), horizontal-high (HH) and vertical-high (VH) subbands. For the LL-subband, a low-pass filter is applied in both the horizontal and vertical directions.…”

Section: Texture Granularity Indexmentioning

confidence: 99%

A no reference texture granularity index and application to visual media compression

Subedar

Karam

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Texture granularity is an important attribute to quantify the level of details present in the image. This work presents a no-reference texture granularity index and shows using subjective experiments that the proposed granularity index correlates well with the perceived granularity of textures. In addition, a subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. It is shown that a measure of texture granularity can predict the compression quality.

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Section: Texture Granularity Indexmentioning

confidence: 99%

A no reference texture granularity index and application to visual media compression

Subedar

Karam

2015

2015 IEEE International Conference on Image Processing (ICIP)

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“…As the same as [4], the different filters for significant and insignificant signals are considered based on MMSE estimation. That is (8): if image wavelet coefficients y (i, j) is significant then (4) We can find that the similar estimation was proposed in paper [3]: the low frequency information ( , respectively.…”

Section: Adaptive Filter Based On Significant or Insignificant Signalmentioning

confidence: 99%

“…Up to now, there are two kinds of ways for wavelet denoising. One is the threshold wavelet denoising [2,6,7] and the other is adaptive Wiener filtering [3,4,5] based on minimizing Mean Square Error (MSE) rule. The latter has better performance in image restoration except for the image edge because the signal variance in the edge region is bigger than that in the other region.…”

Section: Regularization By Wavelet Denoisingmentioning

confidence: 99%

“…These methods are mainly iteration algorithm and their results are not perfect, especially in the high frequency domain. Recently multiscale approach [2,3,4,5] using wavelet analysis has been widely used because the approach is well matched the human visual system. On account of this property, the multiscale approach has been applied to the image restoration.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Wiener filtering for image restoration using wavelet package based on the edge detection

Zhang

Liu

et al. 2005

SPIE Proceedings

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In order to utilize the multiscale characteristics of wavelet transform more sufficiently, a new adaptive image restoration method using wavelet packets based on the edge detection is presented. The method allows for the decomposition of image signal with various frequencies in the subband domain. The proposed filters in the paper explicitly incorporate both within and between subband relations of the decomposed image. The adaptive filter is used in the approach for considering local adaptation in each subband of the best basis. In order to keep the high frequency information of the image, the adaptive edge detection is presented in the paper. Experimental results, which could test the proposed method, are got. It is found that the proposed method show not only great noise reduction in the processed images but also significant improvement of subjective image quality over the conventional image restoration methods.

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“…Many denoising methods have been developed over the years [1][2][3][4][5] . Wavelet thresholding (WT) is a popular approach with the following steps: First, an input image is decomposed into its approximation subbands and detail subbands through 2D wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

An image denoising method based on multiscale wavelet thresholding and bilateral filtering

Shi

2010

Wuhan Univ. J. Nat. Sci.

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A novel image denoising method is proposed based on multiscale wavelet thresholding (WT) and bilateral filtering (BF). First, the image is decomposed into multiscale subbands by wavelet transform. Then, from the top scale to the bottom scale, we apply BF to the approximation subbands and WT to the detail subbands. The filtered subbands are reconstructed back to approximation subbands of the lower scale. Finally, subbands are reconstructed in all the scales, and in this way the denoised image is formed. Different from conventional methods such as WT and BF, it can smooth the low-frequency noise efficiently. Experiment results on the image Lena and Rice show that the peak signal-to-noise ratio (PSNR) is improved by at least 3 dB and 0.7 dB compared with using the WT and BF, respectively. In addition, the computational time of the proposed method is almost comparable with that of WT but much less than that of BF.

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Wavelet-based adaptive image denoising with edge preservation

Cited by 15 publications

References 13 publications

A no reference texture granularity index and application to visual media compression

A no reference texture granularity index and application to visual media compression

Adaptive Wiener filtering for image restoration using wavelet package based on the edge detection

An image denoising method based on multiscale wavelet thresholding and bilateral filtering

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