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IEFA—A Fuzzy Framework for Image Enrichment

  • Ankita Sheoran
  • Harkiran Kaur
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In this work, an Image Enhancement Fuzzy Algorithm (IEFA), a technique for image enhancement has been proposed and developed. IEFA formulates the mapping from a given input to an output using fuzzy logic. IEFA improves the contrast of low-contrast images. The technique begins the process of image enrichment by modifying membership functions and designing fuzzy if–then rules that exist as a sophisticated bridge between human knowledge on one side and the numerical framework of the computers on the other side. The algorithm converts image properties into fuzzy data and further fuzzy data into crisp output through defuzzification. Further, to evaluate the performance of the proposed technique, the developed technique has been compared with “Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE).” It has been observed that PSNR and CII of the proposed algorithm (using a test image) are 25.56 and 1.13, respectively. These metrics are 0.078 and 6.603% more effective than the metrics of existing algorithms.

Keywords

Image enhancement Fuzzy logic Membership functions 

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia

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