An edge detection based noise removal algorithm

Ranjbaran, Ali; Hassan, Abu; Kheng, Eng Swee; Ranjbaran, Bahar

doi:10.1016/j.compeleceng.2013.08.007

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…BM3D is a well-engineered algorithm which represents the current state-of-the-art method for denoising images corrupted by Additive White Gaussian Noise (AWGN) (Dabov et al 2007 ; Dabov et al 2006 ; Dabov et al 2008 ; Dabov et al 2009 ; Chen and Wu 2010 ). In another strategy, denoised image is considered as a linear combination of the original image and its average when the coefficients are determined by an edge detector (Ranjbaran et al 2013 ).…”

Section: Related Workmentioning

confidence: 99%

A Laplacian based image filtering using switching noise detector

et al. 2015

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This paper presents a Laplacian-based image filtering method. Using a local noise estimator function in an energy functional minimizing scheme we show that Laplacian that has been known as an edge detection function can be used for noise removal applications. The algorithm can be implemented on a 3x3 window and easily tuned by number of iterations. Image denoising is simplified to the reduction of the pixels value with their related Laplacian value weighted by local noise estimator. The only parameter which controls smoothness is the number of iterations. Noise reduction quality of the introduced method is evaluated and compared with some classic algorithms like Wiener and Total Variation based filters for Gaussian noise. And also the method compared with the state-of-the-art method BM3D for some images. The algorithm appears to be easy, fast and comparable with many classic denoising algorithms for Gaussian noise.

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Section: Related Workmentioning

confidence: 99%

A Laplacian based image filtering using switching noise detector

et al. 2015

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“…This process may become challenging due to the presence of noise in the image. Current edge detection methods are designed for signals with linear additive Gaussian noise and rely on noise removal prior to the edge detection process [1,2]. Recent focus has moved towards nonlinear filter design for edge detection for signal-dependent noise [3,4].…”

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confidence: 99%

Edge localisation in MRI for images with signal‐dependent noise

2015

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A new edge localisation technique for step edges in images with signaldependent Rician noise in MRI is proposed. Dependent noise can not only affect the detection of true edges in an image but also their position. Inaccurate localisation of an edge can lead to insufficient segmentation and reduce the overall accuracy of any edge detection method. The proposed technique uses higher-order moments of the noise function to determine the correction factor for localisation and thus reduce the overall mean square error for true edge localisation due to noise. The proposed technique offers significant improvement in edge localisation for an image as compared with Canny and Sobel methods.

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An edge detection based noise removal algorithm

Cited by 2 publications

References 10 publications

A Laplacian based image filtering using switching noise detector

A Laplacian based image filtering using switching noise detector

Edge localisation in MRI for images with signal‐dependent noise

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