Abstract
Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson’s disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods.
Highlights
Texture plays an important role in the identification of regions of interest in an image, texture enhancement is an essential component in digital image processing [1]
The key differences of our method with previous work [8, 26] are that we apply a Lagrange 3-point interpolation formula on the Riesz fractional differential operator and incorporate variable fractional order into the algorithm
We have shown that the outlined approach can be applied to different data, and the weights can be manipulated to achieve the highest or lowest signal to noise ratio (SNR), Ent, Standard Deviation (STD) or Mean Absolute Difference Coefficient (MADC)
Summary
Texture plays an important role in the identification of regions of interest in an image, texture enhancement is an essential component in digital image processing [1]. The use of a fractional centered difference scheme enables our method to provide higher signal-to-noise ratios and superior image quality than the classical integral order differential mask operators and other first order fractional differential operators, such as YiFeiPU-1 [25]. Results were provided for the Lena image only, and a rigorous quantitative analysis of their findings was not performed They did show that their method had a higher signal-to-noise ratio and superior image quality than the traditional fractional differential operator and classical integral order differential mask operators. The key differences of our method with previous work [8, 26] are that we apply a Lagrange 3-point interpolation formula on the Riesz fractional differential operator and incorporate variable fractional order into the algorithm. The v-order Grünwald-Letnikov based fractional derivative GLDvxsðx; yÞ with respect to x for the finite interval x 2 [a, X] can be expressed by [30, 31]: GL Dvx 1⁄4
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call