Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges. © 2021 IEEE.
Comparison of Different Implementation Methods of Fractional-Order Derivative/Integral
Implementing a fractional-order operator requires many resources to acquire an accurate response compared to the theoretical response. In this paper, three implementation methods of digital fractional-order operators are exploited. The three implementation methods are based on FIR, IIR, and lattice wave digital filters. The three methods are implemented using different optimization algorithms to optimize the choice of the coefficients of the three filters. This optimization is done to approximate the frequency response of an ideal fractional operator. This comparison aims to determine each implementation method’s accuracy and resource usage level to decide which method is better for different systems. © 2021 IEEE.
An Improved Approximation of Grunwald-Letnikov Fractional Integral
Fractional calculus increases the flexibility of a system by studying the unexplored space between two integers
An Optimized Implementation of GL Fractional-Order
An alternative implementation of of fractional derivative/integral defined by Grunwald-Letnikov definition (GL) is introduced
High-performance fractional anisotropic diffusion filter for portable applications
Anisotropic diffusion is one of the most effective methods used in image processing. It can be used to eliminate the small textures of an image while preserving its significant edges. In this paper, a new anisotropic diffusion filter is proposed based on a fractional calculus kernel rather than integer kernel to improve the overall performance of the filter. Integer and fractional anisotropic filters are implemented using the Genesys-2 FPGA kit to utilize the efficiency of parallelism in FPGAs. Integer and fractional anisotropic filters are tested against the achievable PSNR value vs the number of iterations. The proposed fractional anisotropic filter has a better PSNR value using a smaller number of iterations, reducing the power and area compared to integer anisotropic filter. The proposed filter can be used in image smoothing, edge detection, image segmentation, image denoising, and cartooning. In addition, the proposed filter reduces the power consumption by 58.2% compared to integer-order filters, which makes the proposed filter suitable for battery-based applications. © 2023, The Author(s).