Earticle

초록

영어

The contrast of an image is a feature which determines how image looks better visually. In this paper, we are analysing the capability of activation functions for contrast enhancement. Activation functions are classically used in neural network. In this paper, Activation function creates a mask which is operated on the image on pixel by pixel basis. On the basis of activation function the pixel value of image is changed which improves the contrast of image. We have used various activation functions such as sigmoid function, bipolar sigmoid function, RAMP function, hyperbolic tangent function. Contrast enhancement using these activation functions has been successfully applied on several dark and bright images. For performance assessment we have used Peak Signal to Noise Ratio (PSNR), absolute mean brightness error (AMBE), and Structure Similarity Index (SSIM). From experimental result, it is observed that RAMP function and hyperbolic tangent function have better image enhancement capability.

목차

Abstract
 1. Introduction
 2. Histogram Equalization (HE)
 3. Activation Function
  3.1. Uni Sigmoid Function
  3.2. Bi-polar Sigmoid
  3.3. Hyperbolic Tangent Function
  3.4. Ramp Function
 4. Activation function based algorithm
 5. Performance Evaluation
  5.1. Peak Signal to Noise Ratio (PSNR)
  5.2. Absolute Mean Brightness Error(AMBE)
  5.3. Structure Similarity Index (SSIM)
 6. Results and Discussion
 7. Conclusion
 References

키워드

저자

• Gourav Garg [ Department of C.S.E. & I.T. Madhav Institute of Technology and Science, Gwalior, India ]
• Poonam Sharma [ Department of C.S.E. & I.T. Madhav Institute of Technology and Science, Gwalior, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605