Bayesian Networks for Edge Preserving Salt and Pepper Image Denoising

Noise is a random variation in brightness and color in image or simply we can say that unwanted signals are called noise. The noise is mixed with original signal and cause may troubles. Due to the presence of noise, quality of image is reduced and other features like edge sharpness and pattern recognition are badly affected. In image denoising methods to improve the results a hybrid filter is used for better visualization. The hybrid filter is composed with the combination of three filters connected in series. The hybridization has performed much better in case of salt and pepper type of noise and for most of the medical image type, either MRI, CT, SPECT, Ultra Sound. PSNR values show major improvement in comparison of other existing methods. Future, the results obtained from the presented denoising experiments would be tried to be improved further by using this method with other transform domain methods. Finally, the results are concluded that the proposed approach in terms of PSNR, MSE improvement is outperformed.

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Deducing Local Influence Neighbourhoods with Application to Edge-Preserving Image Denoising

Graph-Based Representations in Pattern Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72903-7_17 ◽

2007 ◽

pp. 180-190

Author(s):

Ashish Raj ◽

Karl Young ◽

Kailash Thakur

Keyword(s):

Image Denoising ◽

Local Influence ◽

Edge Preserving

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A well-posed multiscale regularization scheme for digital image denoising

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-011-0061-7 ◽

2011 ◽

Vol 21 (4) ◽

pp. 769-777 ◽

Cited By ~ 11

Author(s):

V. Prasath

Keyword(s):

Image Denoising ◽

Digital Image ◽

Regularization Parameter ◽

Input Image ◽

Smooth Transition ◽

Functions Of Bounded Variation ◽

Edge Preserving ◽

Regularization Scheme ◽

Adaptive Parameter ◽

Well Posed

A well-posed multiscale regularization scheme for digital image denoisingWe propose an edge adaptive digital image denoising and restoration scheme based on space dependent regularization. Traditional gradient based schemes use an edge map computed from gradients alone to drive the regularization. This may lead to the oversmoothing of the input image, and noise along edges can be amplified. To avoid these drawbacks, we make use of a multiscale descriptor given by a contextual edge detector obtained from local variances. Using a smooth transition from the computed edges, the proposed scheme removes noise in flat regions and preserves edges without oscillations. By incorporating a space dependent adaptive regularization parameter, image smoothing is driven along probable edges and not across them. The well-posedness of the corresponding minimization problem is proved in the space of functions of bounded variation. The corresponding gradient descent scheme is implemented and further numerical results illustrate the advantages of using the adaptive parameter in the regularization scheme. Compared with similar edge preserving regularization schemes, the proposed adaptive weight based scheme provides a better multiscale edge map, which in turn produces better restoration.

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