A novel method for video enhancement under low light using BFR-SEQT technique

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.