A Review on Deep Image Contrast Enhancement

Authors

  • Puspad Kumar Sharma M.Tech. Scholar, Department of CSE, National Institute for Interdisciplinary Science and Technology, Bhopal, M.P, India
  • Nitesh Gupta Assistant Professor,Department of CSE, National Institute for Interdisciplinary Science and Technology, Bhopal, M.P, India
  • Anurag Shrivastava Associate Professor, Department of CSE, National Institute for Interdisciplinary Science and Technology, Bhopal, M.P, India

DOI:

https://doi.org/10.24113/ijoscience.v6i1.258

Keywords:

Image enhancement, Image quality, Deeplearningapproaches, Digital image processing.

Abstract

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

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References

[1]P. Cunningham, M. Cord and S. J. Delany, “Supervised Learning,” in Machine Learning Techniques for Multimedia Case Studies on Organization and Retrieval, Springer, 2008.
[2]G. Cao, L. Huang, H. Tian, X. Huang, Y. Wang and R. Zhi, “Contrast enhancement of brightness distorted images by improved adaptive gamma correction,” Computers & Electrical Engineering, vol. 66, pp. 569-582, 2018.
[3]L. Shen, Z. Yue, F. Feng, Q. Chen, S. Liu and J. Ma, “MSR-net:Low-light Image Enhancement Using Deep Convolutional Network,” ARXIV, 2017.
[4]X. Guo, Y. Li and H. Ling, “LIME: Low-Light Enhancement via Illumination Map Estimation,” IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 982-993, 2016.
[5]X. Guo, “LIME: Low-light IMage Enhancementvia Illumination Map Estimation,” Google, [Online]. Available: https://sites.google.com/view/xjguo/lime. [Accessed 17 April 2018].
[6]J. Lim, J.-H. Kim, J.-Y. Sim and C.-S. Kim, “Robust contrast enhancement of noisy low-light images: Denoising-enhancement-completion,” in 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, 2015.
[7]Y. Yoo, J. Im and J. Paik, “Low-Light Image Enhancement Using Adaptive Digital Pixel Binning,” Sensors -Open Access Journal, pp. 14917-14931, 15 September 2015.
[8]Ramiz, M.A., Quazi, R.: Design of an efficient image enhancement algorithms using hybrid technique. Int. J. Recent Innov. Trends Comput. Commun. 5(6), pp. 710–713, 2017.
[9]E. Schwartz, R. Giryes and A. M. Bronstein, “DeepISP: Learning End-to-End Image Processing Pipeline,” 2018.
[10]CS231n, “Convolutional Neural Netowrks (CNNs/ConvNets),” Github, [Online]. Available: http://cs231n.github.io/convolutional-networks/. [Accessed 24 March 2018].

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Published

01/08/2020

How to Cite

Sharma, P. K., Gupta, N., & Shrivastava, A. (2020). A Review on Deep Image Contrast Enhancement. SMART MOVES JOURNAL IJOSCIENCE, 6(1), 19–22. https://doi.org/10.24113/ijoscience.v6i1.258