A new fractional-order variational model for speckled de-noising

Hacini, Meriem; Hachouf, Fella; Djemal, Khalifa

doi:10.1109/euvip.2014.7018384

Cited by 2 publications

(7 citation statements)

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“…In this part, a de‐noising process is carried out using Hacini et al . algorithm [45]. This algorithm has been designed for speckled images as ultrasound knowing that speckle noise has a multiplicative form, therefore, difficult to suppress.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Speckle has a negative effect on medical ultrasound images. It has a tendency to reduce the contrast hence making image details obscure and blur, affecting in this way the human ability in identifying normal and abnormal tissues [45]. In this section, the proposed 2D‐AFCD has been applied to both natural and real ultrasound images.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The considered classical significant operators are G-L [6] and Sobel [40]. [45]: In this section, the first-order gradient is replaced by the fractional-order gradient in the regularisation equation of the model in [41]. Thus, the fractionalorder variational de-noising model is described as follows: details of the used algorithm are in [45].…”

Section: Using the 2d-afcd Operator In Speckle Reductionmentioning

confidence: 99%

“…Hacini et al . [20] have elaborated on a new fractional‐order variational model for ultrasound image de‐noising. In [21], Dazi et al .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, variational models have been concerned by fractional order. Hacini et al [20] have elaborated on a new fractional-order variational model for ultrasound image de-noising. In [21], Dazi et al proposed weighted split Bregman iterations for an adaptive fractional-order total variation model.…”

Section: Introductionmentioning

confidence: 99%

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A bi‐directional fractional‐order derivative mask for image processing applications

2020

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Fractional computation has been recently designed as a major mathematical tool in image and signal processing fields. This study presents a novel operator established for two‐dimensional fractional differentiation. It is developed based on the one‐dimensional Charef fractional differentiation extension. A new multi‐directional mask is proposed and a new adaptive fractional‐order computation is introduced. The proposed method uses the gradient computation properties. It has been applied in edge detection and de‐noising problems using real and synthetic images. Obtained results have been compared to those given by integer and fractional useful operators. Results demonstrate that the fractional edge images obtained using the proposed operator has more complete and clear contour information and more abundant texture detail information. The performances have been improved by the proposed method.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%