Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model

Abstract

Marine resources are one of the valuable resources that need to be explored and protected, but are affected due to environmental factors. The underwater images are distorted due to the light scattering and absorption phenomenon, which draws the attention of the researcher to restoring the underwater images which is a challenging task. High-quality underwater images help us to learn and study deep-sea environments, and also help in gaining deep knowledge about the ocean resources. In this research, deep learning methods are used for obtaining high-quality underwater images. Accordingly, a channel-based white balancing and social situation-aware guard optimization (WBSSGO) dependent multi-variant balancing color correction model is proposed by integrating the grouping behavior and adaptability nature of the guards and flocks, which acts as the search agents in the proposed optimization. A multi-variant-based white balancing model is employed for balancing the channels and correcting the distorted underwater images. The performance of the developed model is validated using PIQE, BRISQUE, and UCIQE, which acquired minimal values of 27.202, 24.692, and 29.725, respectively with comparative methods, while the higher value of SDME is 64.049 dB for the proposed method, which is reported to acquire the higher visual quality of underwater images.