Abstract
ABSTRACT As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light areas' boundary pixels and varying low-illuminated night videos' weather conditions for removing noise and enhancing contrast. Hence, the video's visual appearance under low-light is improved using BFR-SEQT. Primarily, a video is inputted and converted into frames. Also, colour space is converted from which static and dynamic pixels are detected regarding frame differences. Using LoG-MF and KF algorithms, noise is removed from which foreground and background are separated using SD-FCM. Motion is estimated and features are extracted to enhance contrast. Then, pixel grouping using LC-GOA is done. Lastly, enhanced outputs from both phases are reconstructed and enhanced video is obtained. The proposed model improves video quality by enhancing contrast and removing noise with high PSNR values (27.6589db and 24.5478db), thus outperforming conventional methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
