In this paper, a fractional differential-based approach for CT image enhancement is introduced. This approach uses a group of fractional differential masks, which are generalized from the one-dimensional digital fractional order Savitzky-Golay differentiator, to process the image and a max-saturation strategy is designed to enhance these processed images. Some experiments are used to assess the performance of the proposed fractional differential-based image enhancing algorithm, and the results demonstrate that the proposed enhancing method is able to achieve a good tradeoff between the feature enhancement and texture preservation.
An Improved Grunwald-Letnikov Fractional Differential Mask for Image Texture Enhancement