Texture enhancement algorithm based on fractional differential mask of adaptive non-integral step

Si, Maoxin; Fang, Ligang; Hu, Fuyuan; Si, Shaohui

doi:10.1109/cisp.2014.7003773

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…It has been shown that in many applications, using fractional-order derivatives rather than integer-order derivatives enables us to have more efficient models, because the differentiation order can be changed to find out optimal performance. Many fractional-order-based methods have been used in various applications, such as image texture enhancement, 29,30 image restoration, [31][32][33] study of behavior for immunogenetic tumor model, 34,35 image edge detection, 36 study of population model, 37,38 image segmentation, 39 image compression, 40 etc. 41,42 A good review of fractional calculus applications in image processing has been gathered in the literature 43 by Qi Yang.…”

Section: Proposed Methodsmentioning

confidence: 99%

Image deblurring using adaptive fractional‐order shock filter

Irandoust‐pakchin

Babapour

Lakestani

2020

Math Methods in App Sciences

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In this paper, a novel image enhancement model based on shock filter for image deblurring is proposed in three cases. For the weight of shock filter, the fractional‐order derivative of initial blurry image is used. This fractional‐order weight can be adjusted adaptively according to the gradient of blurred image. Compared with the traditional integer‐order shock filter, the proposed model creates less staircase effect, whereas the enhancement and deblurring of the resulted images are efficiently good. Stability properties of the proposed method are studied theoretically, and also to show the validity of stability studies results, some numerical experiments are presented. The three cases of proposed method are compared with together and with other methods. Different test images are used to show the validity of these cases. Numerical experiments confirm the efficiency of these cases.

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Section: Proposed Methodsmentioning

confidence: 99%

Image deblurring using adaptive fractional‐order shock filter

Irandoust‐pakchin

Babapour

Lakestani

2020

Math Methods in App Sciences

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“…In recent years, researchers have tried to enhance texture details in images by using fractional di®erential. 27 Because fractional di®erential can not only properly enhance the high and moderate frequency part of signal, but also not sharply attenuate very low frequency part of the signal which is nonlinear reserved to some extent. So fractional di®erential can extract detail texture in di®erent smoothness regions of the image, particularly in the smooth area of image.…”

Section: Detail Texture Information Extractionmentioning

confidence: 99%

Haptic Display of Image Based on Multi-Feature Extraction

Tian

Song

Chen

et al. 2016

Int. J. Patt. Recogn. Artif. Intell.

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Image feature extraction is one of the key technologies of image haptic display. In this paper, multi-feature extraction method of the object in image is proposed to improve image-based haptic perception. The multi-feature extraction includes contour shape extraction, pattern extraction and detail texture extraction. Firstly, we use an intrinsic decomposition method to decompose an image into shading image and re°ectance image. The re°ectance image describes nonillumination a®ected color patterns spread on the surface. Then, the shading image is utilized in contour shape and detail texture extraction. Contour shape extraction is based on partial di®erential equation (PDE), to reconstruct three-dimensional (3D) surface model in virtual environments. Detailed texture extraction is based on fractional di®erential method simultaneously. Finally, the various features extracted above are haptic rendered by di®erent methods. The experimental results show the e®ectiveness and potentiality of the proposed method for improving the ability of haptic perception and recognition of human in virtual environments.

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“…Therefore, the SNR is selected for adjusting the order and size of the fractional-differential mask. Compared with other adaptive fractional-order methods [34][35][36][37], our SNR method is more robust under illumination changes. The SNR of every pixel in the image is sorted in descending order after calculations.…”

Section: 2 Adaptive Order and Size Of The Fraction Order Differentiamentioning

confidence: 99%

“…However, the traditional brightness constraint equation is no longer fit for complex illumination changes, so the fractional‐order derivative was applied to substitute the integer order derivative for enhancing the robustness under illumination changes:

D_{x}^{α} I \cdot u^{α} + D_{y}^{α} I \cdot ν^{α} + D_{t}^{α} I = 0,

where

D_{x}^{α} I

D_{y}^{α} I

, and

D_{t}^{α} I

denote the fractional‐order derivatives of brightness function I on the

x, y, t

axes, respectively;

α \in R^{+}

, denotes the order of fractional‐order derivative. More details about the rationality of (3) can be found in [35].…”

Section: Construction Of Dfovof Modelmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive dual fractional‐order variational optical flow model for motion estimation

Zhu

et al. 2019

IET Computer Vision

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Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high-performance adaptive dual fractional-order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional-order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn-Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional-order differential masks. The domain of each region is determined adaptively. The order and size of the fractional-order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional-order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional-order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms on low-light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.

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Texture enhancement algorithm based on fractional differential mask of adaptive non-integral step

Cited by 6 publications

References 9 publications

Image deblurring using adaptive fractional‐order shock filter

Image deblurring using adaptive fractional‐order shock filter

Haptic Display of Image Based on Multi-Feature Extraction

Adaptive dual fractional‐order variational optical flow model for motion estimation

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