ACCE: An Adaptive Color Compensation and Enhancement Algorithm for Underwater Image

Underwater images often suffer from significant information loss in the red color channel, resulting in a predominantly bluish or greenish tone. Existing enhancement methods struggle to address this issue due to uniform enhancement applied to the bluish and greenish channels, resulting in overcompensation or under-compensation in the red channel. To address these challenges and achieve a more natural color restoration in underwater images, we propose the adaptive color compensation and enhancement (ACCE) algorithm. The ACCE algorithm comprises several essential steps. Initially, to recover the loss of red channel information more effectively, we divide the images into bluish and greenish components for preliminary color compensation (PCC) in the RGB color space. Subsequently, we introduce a novel minimum color loss (MCL) constraint to regulate the PCC, ensuring balanced histogram distributions across the RGB channels. Furthermore, for improved color balance in the enhanced underwater image, we design the fine-tuning color compensation (FCC) to the a and b channels of the CIELAB color space. Ultimately, we employ the Contour Bougie (CB) enhancement algorithm to restore contour details in underwater images. Experimental results validate the superiority of the proposed ACCE algorithm over state-of-the-art methods, as demonstrated through qualitative and quantitative comparisons. In addition, ACCE exhibits promising generalization and potential for broader applications, encompassing tasks such as dehazing and lowlight image enhancement.