Using diffusion equations for improving performance of wavelet-based image denoising techniques

Nikpour, Mohsen; Hassanpour, Hamid

doi:10.1049/iet-ipr.2009.0180

Cited by 21 publications

(5 citation statements)

References 18 publications

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“…I 0 (x,y)is image pixel values, Perona-Malik (PM) [4] suggested the two well-known diffusion coefficient as follows:…”

Section: X Y T Div D I X Y T I X Y T T I X Y I X Ymentioning

confidence: 99%

“…So in this study, both wavelet threholding and anisotropic diffusion equation are applied for image de nosing. Recently M. Nikpour .al (2010) posed using diffusion equations for improving performance of wavelet-based image de noising techniques [4]. They use the classical anisotropic diffusion equation and wavelet transform for image de noising, the proposed method presents a better performance compared to the existing wavelet thresholding method, anisotropic diffusion, fourth-order PDE and median filter in image de noising.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Anisotropic Diffusion Model Based on Wavelet Transform for Image Denosing

Zhang

Liu

et al. 2012

AMR

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Preserving meaningful details such as blurred thin edges and low-contrast fine features is important in image de noising. A new method based on improved anisotropic diffusion model and wavelet transform is presented for image denoising. The proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise in low-contrast surface images.

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“…I 0 (x,y)is image pixel values, Perona-Malik (PM) [4] suggested the two well-known diffusion coefficient as follows:…”

Section: X Y T Div D I X Y T I X Y T T I X Y I X Ymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Anisotropic Diffusion Model Based on Wavelet Transform for Image Denosing

Zhang

Liu

et al. 2012

AMR

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“…Conversely, the frequency domain methods that use wavelets frequently obtain more satisfying results. Because wavelets exhibit superior time-frequency characteristics and multi-resolution characteristics, it is extensively applied in the field of image denoising [4,5]. The frequency-based denoising methods have achieved a better denoising effect.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Image Denoising Via the Improved Total Variation (TV) Algorithm

Zhu¹,

Wang²,

Liu³

2016

Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials

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This paper proposes several improvements for a fingerprint image denoising method that is based on nonlocal total variation (TV) models using split Bregman iteration. The main improvement involves the addition of relaxation factors to the two-step iterative process of split Bregman iterative algorithms to obtain a double relaxation split Bregman iterative algorithm. The improved method is tested using numerous fingerprint images from FVC2004 databases. The experimental results show that the improved double relaxation split Bregman iterative algorithm achieves significantly better performance in terms of the visual subjective evaluation and the quantitative objective evaluation. The method achieves better noise suppression and effectively retains image edge details.

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“…The most popular methods include Lee filter [14], Kuan et al method [15] and anisotropic diffusion [6]. A lot of work has been done with anisotropic diffusion equations in such a way that the important structural information can be retained in the filtered image [8,[16][17][18]. Buades et al proposed a new filtering technique, named non-local means algorithm [19] which works well in Gaussian noise reduction with edge preservation because of using the region comparison instead of pixel comparison.…”

Section: Introductionmentioning

confidence: 99%

Speckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain

Gupta

Anand

Tyagi

2015

IET Image Processing

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Speckle filtering is of great interest for the ultrasound medical images in which various noises and artefacts are introduced because of various limitations of the acquisition systems and techniques. Speckle is a prime factor to degrade the quality and most importantly, texture information present in the ultrasound images. This study presents a despeckling method based on a modified non-linear diffusion model and non-subsampled shearlet transform (NSST). As a new image representation method with the different features of localisation, directionality and multiscale, the NSST is utilised to provide the effective representation of the image coefficients. The modified anisotropic diffusion is applied to the noisy coarser NSST coefficients to improve the denoising efficiency and preserve the edge features effectively. In the diffusion process, the nonlocal pixel information is incorporated to evaluate the gradient of eight connected neighbouring pixels with an adaptive grey variance. The performance of the proposed method is evaluated for both the standard test and real ultrasound images. Experimental results show that the proposed method produces better results of noise suppression with the preservation of more edges compared with several existing methods.

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Using diffusion equations for improving performance of wavelet-based image denoising techniques

Cited by 21 publications

References 18 publications

An Improved Anisotropic Diffusion Model Based on Wavelet Transform for Image Denosing

An Improved Anisotropic Diffusion Model Based on Wavelet Transform for Image Denosing

Fingerprint Image Denoising Via the Improved Total Variation (TV) Algorithm

Speckle filtering of ultrasound images using a modified non‐linear diffusion model in non‐subsampled shearlet domain

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