Fractional derivative based Unsharp masking approach for enhancement of digital images
- PDF / 2,864,435 Bytes
- 35 Pages / 439.37 x 666.142 pts Page_size
- 81 Downloads / 185 Views
Fractional derivative based Unsharp masking approach for enhancement of digital images Kanwarpreet Kaur 1 & Neeru Jindal 1 & Kulbir Singh 1 Received: 20 November 2019 / Revised: 13 August 2020 / Accepted: 2 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Image visual quality is severely degraded due to various environmental conditions, thus, leading to the loss in image details. Therefore, an image enhancement approach is required to improve the visual quality of images. In this paper, Unsharp Masking (UM) approach based on Riemann-Liouville (RL), Grunwald-Letnikov (GL), and Riesz fractional derivatives is proposed for the image enhancement. The fractional derivatives based UM approach sharpened the edges of an image while preserving its low and medium frequency details. Furthermore, the extra parameter of fractional derivative provides an additional degree of freedom, thus, increasing the effectiveness of the proposed approach. Extensive simulations carried out on several standard images of different sizes validated the performance of proposed approach in comparison to the existing techniques. The capability of the proposed approach is further confirmed by considering the test images with varying illumination conditions. Moreover, the comparative analysis performed in terms of quantitative measures such as Information Entropy (IE), Average Gradient (AG), Measure of Enhancement (EME), etc. confirmed that the proposed UM approach based on Riesz fractional derivative outperforms the existing state-of-the-art image enhancement techniques. Furthermore, the potential of the proposed approach is validated by considering its application in the medical images. Keywords Average gradient . Fractional derivative . Information entropy . Measure of enhancement . Unsharp masking
* Kulbir Singh [email protected] Kanwarpreet Kaur [email protected] Neeru Jindal [email protected]
1
Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Multimedia Tools and Applications
1 Introduction Image enhancement plays a significant role in various fields of image processing such as image restoration, image compression, medical imaging, underwater imaging, remote sensing, etc. [29, 32, 40, 43–45]. The image enhancement techniques are primarily used to smooth the irregularities present in the original image with a minimal change in the image information. Moreover, it leads to the sharpening of features such as contrast, edges, texture, boundaries thus increasing their dynamic range for easier detection [32]. Since human perception is extremely sensitive to the edges present in an image. Therefore, any kind of reduction in high-frequency components leads to the degradation of visual quality of an image. Hence, it is also necessary to enhance the edges of an image. Generally, image enhancement techniques such as Unsharp Masking (UM), high boost filtering, etc. are considered to improve the visual quality of edges
Data Loading...
