Single underwater image enhancement based on differential attenuation compensation

High quality underwater images and videos are important for exploitation tasks in the underwater environment, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. To correct the chromatic aberration and enhance the sharpness of underwater images in order to improve the quality of underwater images, we based on the differential compensation proposed a Differential Attenuation Compensation (DAC) method. The underwater image is contrast stretched to improve the contrast of the image, as well as the underwater image is denoised, for the red channel with serious loss of detail information we choose the blue and green channels with more detail information to compensate for this, and finally the image is restored through the grayscale world to obtain more realistic colors. Our method is qualitatively and quantitatively compared with multiple state-of-the-art methods in the public underwater image dataset, underwater image enhancement benchmark (UIEB) and enhancing underwater visual perception (EUVP), demonstrating that the underwater images processed by our method better resolve the problems of chromatic aberration and blur, with more realistic color, detail and better underwater image quality evaluation indicators

Automated summary

High-quality underwater images and videos are crucial for underwater exploitation tasks, but the complexity of the underwater imaging environment often results in low-quality images. In order to improve the quality of underwater images by correcting chromatic aberration and enhancing sharpness, we propose a Differential Attenuation Compensation (DAC) method based on differential compensation. Our method stretches the contrast and denoises the underwater image, compensating for the loss of detail information in the red channel by utilizing the more detailed blue and green channels, and finally restores the image to achieve more realistic colors. Comparisons with state-of-the-art methods in public datasets demonstrate that our method effectively addresses chromatic aberration and blur issues, resulting in underwater images with more realistic colors, details, and improved quality evaluation indicators.