Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

Authors

  • Puspad Kumar Sharma MTech Scholar Department of CSE NIIST, Bhopal, India
  • Nitesh Gupta Assistant Professor Department of CSE NIIST, Bhopal, India
  • Anurag Shrivastava Associate Professor Department of CSE NIIST, Bhopal, India

DOI:

https://doi.org/10.24113/ojssports.v7i1.116

Abstract

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.

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References

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Published

2020-02-10

How to Cite

Sharma, P. K., Gupta, N., & Shrivastava, A. (2020). Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function. IJOSTHE, 7(1), 8. https://doi.org/10.24113/ojssports.v7i1.116