Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Abstract

Abstract Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV and HLS colour spaces and then by using the S & V channels in HSV and L & S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE), structural similarity (SSIM), and the underwater colour image quality evaluation metric (UCIQE) and underwater image quality measure (UIQM). Since our proposed approach uses traditional image processing methods, it is computationally less expensive and quicker as compared to deep learning or frequency domain-based methods. With this, it can be adapted for using in real-time applications such as underwater navigation, examination of the behavior of marine ecosystems and other scientific research.