Multiscale LMMSE-based image denoising with optimal wavelet selection

Zhang, Lei; Bao, Paul; Wu, Xiaolin

doi:10.1109/tcsvt.2005.844456

Cited by 91 publications

(36 citation statements)

References 30 publications

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“…One widespread method exploited for speckle reduction is wavelet shrinkage, including VisuShrink (Donoho 1994), SureShrink (Donoho 1995) and BayeShrink (Chang 2000). A wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) is proposed in (Zhang 2005), where an interscale model, the wavelet coefficients with the same spatial location across adjacent scales, was combined as a vector, to which the LMMSE in then applied.…”

Section: Wavelet Filtersmentioning

confidence: 99%

Performance analysis of speckle ultrasound image filtering

Rosa

Monteiro

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Over the last three decades, several despeckling filters have been developed by researchers to reduce the speckle noise inherently present in ultrasound B-scan images without losing the diagnostic information. This paper compiles and compares well-known techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. A comparison of the methods studied is done based on an experiment, using quality metrics, texture analysis and interpretation of row profiles to evaluate their performance and show the benefits each one can contribute to denoising and feature preservation. To test the methods, a noise-free image of a kidney is used and then the Field II program simulates a B-mode ultrasound image. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of seventeen different speckle reduction algorithms have been documented based on spatial filtering, diffusion filtering and wavelet filtering, with fifteen qualitative metrics estimation. We use the tendencies observed in our study in real images. A new evaluation metric is proposed to evaluate the despeckling results.

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Section: Wavelet Filtersmentioning

confidence: 99%

Performance analysis of speckle ultrasound image filtering

Rosa

Monteiro

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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“…One widespread method exploited for speckle reduction is wavelet shrinkage, including VisuShrink, SureShrink [3] and BayeShrink [1]. A wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) is proposed in [20], where an interscale model, the wavelet coefficients with the same spatial location across adjacent scales, was combined as a vector, to which the LMMSE in then applied.…”

Section: Speckle Filtering Techniquesmentioning

confidence: 99%

Towards a Comprehensive Evaluation of Ultrasound Speckle Reduction

Monteiro

Rufino

Cadavez

2014

Lecture Notes in Computer Science

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“…Besides, according to experimental results on SSIM value, the denoising performance is good especially when the image has lots of textures. To prove the performance of our WMFoE method further, we use BM3D [11], LMMSE [12], GHP [13] and FoE [4] for comparison. The codes of all the method above for comparison is provided by the authors on the Internet and we use the authors' parameters setting.…”

Section: B Image Denoisingmentioning

confidence: 99%