Enhanced Image Vision and Resolution during Low Light Conditions using GANs
M Prakash1, Aparna S2, Yash Srivastava3

1Dr. M. Prakash, Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.
2Aparna S, Department of Computer Science and Engineering, SRM Institute of Science and Technology Kattangulathur, Tamil Nadu, India.
3Yash Srivastava, Department of Computer Science and Engineering SRM Institute of Science and Technology, Kattangulathur, Tamil Nadu, India. 

Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1544-1547 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9859038620/2020©BEIESP | DOI: 10.35940/ijrte.F9859.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Low light computer vision is an arduous task because of the low signal to noise ratio and less photon count. This means that the images captured in low light experience noise, which can result in blurring of the image. Although there are multiple techniques to overcome the noise and blur, their results are bounded in undue conditions as in there is a drop in the video imaging at night. This low light enhancement is a daring task as there are multiple factors like brightness, de-noising, de-blurring, contrast must be handled at the same time. Even the development of a CNN has proved to perform poorly on such data. This paper uses a technique to take care of this issue using GANs. Our technique gives a platform to enhance the image captured in low light and increase its resolution giving out an enhanced super resolute image. To support the low light image processing, we have used a dataset of low-light images. This method can give promising results on the dataset, and display a break for the future work. 
Keywords: Random Forest, Data Mining, Classification Algorithms, WEKA, Tuning, Accuracy.
Scope of the Article: Classification