Underwater Video Enhancement Using Manta Ray Foraging Lion Optimization-Based Fusion Convolutional Neural Network

Abstract

Due to the significance of aquatic robotics and marine engineering, the underwater video enhancement has gained huge attention. Thus, a video enhancement method, namely Manta Ray Foraging Lion Optimization-based fusion Convolutional Neural Network (MRFLO-based fusion CNN) algorithm is developed in this research for enhancing the quality of the underwater videos. The MRFLO is developed by merging the Lion Optimization Algorithm (LOA) and Manta Ray Foraging Optimization (MRFO). The blur in the input video frame is detected and estimated through the Laplacian’s variance method. The fusion CNN classifier is used for deblurring the frame by combining both the input frame and blur matrix. The fusion CNN classifier is tuned by the developed MRFLO algorithm. The pixel of the deblurred frame is enhanced using the Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS filter). The developed MRFLO-based fusion CNN algorithm uses the metrics, Underwater Image Quality Measure (UIQM), Underwater Color Image Quality Evaluation (UCIQE), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) for the evaluation by varying the blur intensity. The proposed MRFLO-based fusion CNN algorithm acquired a PSNR of 38.9118, SSIM of 0.9593, MSE of 3.2214, UIQM of 3.0041 and UCIQE of 0.7881.