A Review on Deep Image Contrast Enhancement
Keywords:Image enhancement, Image quality, Deeplearningapproaches, Digital image processing.
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 . 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.
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.
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.
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.
X. Guo, “LIME: Low-light IMage Enhancementvia Illumination Map Estimation,” Google, [Online]. Available: https://sites.google.com/view/xjguo/lime. [Accessed 17 April 2018].
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.
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.
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.
E. Schwartz, R. Giryes and A. M. Bronstein, “DeepISP: Learning End-to-End Image Processing Pipeline,” 2018.
CS231n, “Convolutional Neural Netowrks (CNNs/ConvNets),” Github, [Online]. Available: http://cs231n.github.io/convolutional-networks/. [Accessed 24 March 2018].
How to Cite
Copyright (c) 2020 Vishnu Kumar Patidar, Mr. Anurag Khare
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.