Underwater Image Dehazing via Red-Channel Recovery
Elif Duygu Petenkaya, Ozge Basak Lacin, Omer Faruk Kara, Mehmet Türkan · 2023
Due to light scattering and absorption while traveling through water, underwater images become hazy and loose critical information resulting in poor contrast and weak color performance. Hence, it is difficult to see the difference between foreground colors and items, and to differentiate the background in these images. To solve these issues, this study proposes a novel technique for single underwater image enhancement which relies on the recovery of the lost red-channel via a weighted multi-scale fusion. Firstly, three color balance algorithms are applied to the input image to gain more information about the scene. Then, five weight maps are extracted from these balanced versions of the input image to emphasize fine-details. Finally, the enhanced output is obtained with the new red-channel, white balanced green and blue channels followed by gamma correction to maintain contrast of the image. The developed method produces higher-quality underwater images that can be evaluated qualitatively and quantitatively when compared to state-of-the-art approaches.