An Image-Enhancement System Based on Noise Estimation

Russo, F.

doi:10.1109/tim.2007.899887

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…Under an appointed selecting condition (see Eq. (8) in Russo, 2007), the relationship between σ and this parameter is nearly linear, which is the estimation principle for σ.…”

Section: Combined Wavelet and Curvelet Schemementioning

confidence: 87%

See 1 more Smart Citation

Comparisons of wavelets, contourlets and curvelets in seismic denoising

Shan¹,

Ma²,

Yang³

2009

Journal of Applied Geophysics

112

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“…Under an appointed selecting condition (see Eq. (8) in Russo, 2007), the relationship between σ and this parameter is nearly linear, which is the estimation principle for σ.…”

Section: Combined Wavelet and Curvelet Schemementioning

confidence: 87%

“…As for actual data set, the parameter σ in Step 1 can be estimated by e.g. an algorithm that is based on piecewise linear filtering (Russo, 2007). A piecewise linear filtering takes into account the luminance differences between the central pixel and the neighbors for a given subset of pixels.…”

Section: Combined Wavelet and Curvelet Schemementioning

confidence: 99%

Comparisons of wavelets, contourlets and curvelets in seismic denoising

Shan¹,

Ma²,

Yang³

2009

Journal of Applied Geophysics

112

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“…The reason for considering only the finest level is that the corresponding empirical wavelet coefficients tend to consist mostly of noise. Since there is some signal present even at this level, Donoho and Johnstone proposed a robust estimate of the noise level using the following equation [15], [16], [17], [14].…”

Section: Denoising Algorithmmentioning

confidence: 99%

Speckle Reduction in Carotid Artery Images using Dual Tree Complex Wavelet Transform

Raj¹,

Geetha²

2018

IJCA

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Carotid artery disease is a condition in which the carotid arteries become narrowed or blocked. When the arteries become narrowed, the condition is called carotid stenosis. The carotid arteries provide the main blood supply to the brain. Carotid artery disease occurs when sticky, fatty substances called plaque build-up in the inner lining of the arteries. The plaque may slowly block or narrow the carotid artery or cause a clot (thrombus) to form more suddenly. Clots can lead to stroke. Imaging techniques have long been used for assessing and treating cardiac and carotid disease. B-mode ultrasound imaging or intravascular ultrasound (IVUS) has emerged, and it is widely used for visualizing carotid plaques and assessing plaque characteristics that are related to the onset of neurological symptoms. In Medical diagnosis operations such as feature extraction and object recognition will play the key role. These tasks will become difficult if the images are corrupted with noises. So the development of effective algorithms for noise removal became an important research area in present days. Developing Image denoising algorithms is a difficult task since fine details in a medical image embedding diagnostic information should not be destroyed during noise removal. The ultrasound imaging suffers from speckle noise. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance and lack of directionality. To overcome this in this paper we are using the denoising method which uses dual tree complex wavelet transform to decompose the image and we performed the shrinkage operation to eliminate the noise from the noisy image. In the shrinkage step we used semi-soft thresholding operator along with traditional hard and soft thresholding operators and verified the suitability of dual tree complex wavelet transform for the denoising of ultrasound images. The results proved that the denoised image using DTCWT (Dual Tree Complex Wavelet Transform) have a better balance between smoothness and accuracy than the DWT and less redundant than UDWT (Undecimated Wavelet Transform). We used the SSIM (Structural similarity index measure) along with PSNR to assess the quality of denoised images .[1],[2], [3]

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“…Graph-based enhancement procedures have been introduced in Kheradmand and Milanfar (2014) and Liu et al (2015). An image enhancement method based on piecewise linear directional smoothing and sharpening has been proposed in Russo (2007). Local Laplacian filters in Paris et al (2011) reduce the halo artifacts prevalent in methods based on Laplacian pyramid.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Structure-Aware Image Sharpening with Difference of Smoothing Operators

Kheradmand

Milanfar

2015

Front. ICT

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Citation: Kheradmand A and Milanfar P (2015) Non-linear structure-aware image sharpening with difference of smoothing operators. Front. ICT 2:22. In this paper, we propose an effective data-adaptive filtering mechanism for sharpening of noisy and moderately blurred images. We establish the connection of our proposed data-adaptive filtering procedure with the classic Difference of Gaussians (DoG) operator widely used in image processing and computer graphics. Our proposed filter renders a data adaptive and noise robust version of the classical DoG filter. We also discuss interesting special cases of our general sharpening method. Experimental results verify the effectiveness of the proposed technique for sharpening real images.

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An Image-Enhancement System Based on Noise Estimation

Cited by 17 publications

References 17 publications

Comparisons of wavelets, contourlets and curvelets in seismic denoising

Comparisons of wavelets, contourlets and curvelets in seismic denoising

Speckle Reduction in Carotid Artery Images using Dual Tree Complex Wavelet Transform

Non-Linear Structure-Aware Image Sharpening with Difference of Smoothing Operators

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