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Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images

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Abstract

The main objective of the image enhancement is to improve the visual appearance or quality of an image. In this paper, the proposed scheme aims to improve the performance of the homomorphic filtering by employing the fractional derivatives with Discrete Fourier Transform (DFT) and Fractional Fourier Transform (FrFT). FrFT in combination with fractional derivative provides two fractional orders as extra degrees of freedom, thus, providing more design flexibility. This paper uses Grunwald-Letnikov (GL) fractional derivative to enhance the high and mid frequency components non-linearly while preserving the low frequency components. In the proposed approach, modification of homomorphic filtering technique is done on the basis of fractional derivative and FrFT to enhance the low contrast and non-uniformly illuminated images. The effectiveness of the proposed work is evaluated on the basis of various image assessment parameters such as PSNR, information entropy, universal image quality index, etc. on several images of different sizes. The proposed scheme outperforms the existing state-of-the-art techniques by providing better image visual quality and image information in terms of average PSNR and entropy values. The improvement in the average PSNR and information entropy is in the range 0.2635–50.37 dB and 0.02–42% respectively for standard images as well as for images with different contrast and illumination conditions.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Kanwarpreet Kaur, Neeru Jindal & Kulbir Singh

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  1. Kanwarpreet Kaur
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  2. Neeru Jindal
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  3. Kulbir Singh
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Correspondence to Kulbir Singh.

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Kaur, K., Jindal, N. & Singh, K. Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images. Multimed Tools Appl 78, 27891–27914 (2019). https://doi.org/10.1007/s11042-019-7621-5

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  • DOI: https://doi.org/10.1007/s11042-019-7621-5

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