ABSTRACT
Backlit images simultaneously include dark as well as light regions of intensity. Objects in dark and low-contrast regions are complex for humans to perceive. In particular, such parts are selected as the image's region of interest (ROI). In backlit images, ROI is typically very dark and of low contrast, which makes visualization difficult. This paper explores and presents an approach for contrast enhancement and preserving the naturalness of the ROI of the backlit image. The proposed methodology considers stretching the contrast and balancing other aspects of the ROI, such as textural details, visibility, etc. In the last stage, a fusion-based approach is carried out to obtain enhanced ROI of the backlit image. It also examines the experimental results of the proposed method using various steps and, based on the validation, confirms that the proposed solution produces better results for the ROI of the backlit image than the other state-of-the-art methods.
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No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Gaurav Yadav
Gaurav Yadav is a doctoral degree student in the Department of Computer Science and Engineering, National Institute of Technology (NIT), Jamshedpur, India. He received his M.E. in Automated Manufacturing Systems (inter-disciplinary) from Birla Institute of Technology, Mesra, India in 2015 and his B.Tech in Electronics and Telecommunication engineering from Biju Patnaik University of Technology, Odisha, India in 2012. His current research interests include image processing and machine learning applications in the image enhancement.
Dilip Kumar Yadav
Dilip Kumar Yadav is currently working as a Professor at Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, India. He received his B.Tech (Mechanical, 1991), M.Tech (CAD/CAM, 1994) from NIT Jamshedpur and Ph.D (Software Reliability) from IIT Kharagpur in year 2012. He has research interest in the area of image processing, computer vision, software reliability, machine learning. He has supervised 7 Ph.D thesis and more than 250 M.Tech & MCA thesis. He has published 82 research papers in several international journals and conferences. He is Associate Editor of International Journal of System Assurance Engineering and Management.