Accurate estimation of underwater image restorationbased on dual-background light adaptive fusion and transmission maps

Abstract: Underwater images have been highly required in many application scenarios, such as marine resources exploration, reservoir safety evaluation, and underwater aquaculture monitoring. But the captured images under water are usually degraded, due to the refraction, absorption, and scattering of light by the suspended particles in water. Some limitations of images can be low contrast, blurred details, and color distortion. Therefore, it is urgent for underwater image restoration. Much effort has been made on a variety of underwater image restoration using processing technology so far. However, land image restoration cannot be simply transformed the underwater images using background light estimation. The best restoration cannot be achieved in the current case, such as unnatural restoration, uneven brightness, and reddening color tone of the main body. In this study, an underwater image restoration was proposed using the adaptive fusion of double background lights and accurate estimation of the transmission map. First, two kinds of background lights of the underwater images were obtained using the maximum intensity prior background light estimation method based on underwater light attenuation and Quadtree background light estimation method based on background light flatness. Then, an adaptive fusion algorithm of double background lights was created to realize the accurate estimation of background lights in different scenes. Second, an accurate estimation of the transmission map was established to combine the new underwater dark channel prior, Reversed Saturation Map, and three-channel spectral attenuation coefficient. Furthermore, the restored underwater image was constructed using the obtained fused background light and accurate transmission map. The brightness of the image was adjusted to obtain the final underwater restored image. Finally, the experiment was carried out to verify the model using the aquaculture dataset of Guangdong Tilapia Breeding Farm and Underwater Image Enhancement Benchmark Dataset. The result showed that the improved estimation was better adapted to different underwater environments, thereby improving the contrast and color of the restored underwater image. A better restoration was achieved than the rest, such as dark channel prior, the maximum intensity prior, restoration using blur and light absorption, restoration using blue-green channel defogging, restoration using background light statistics model, and optimization of transmission map. Furthermore, the restoration was evaluated to deal with the current underwater image distortion, dim brightness, and reddish tone. The best value was also achieved among the seven objective evaluation indexes. The Peak Signal to Noise Ratio, Structural Similarity, Mean-Square Error, and Visual Information Fidelity increased by 0.52%, 2.1%, 3.4%, and 0.86%, respectively, in the evaluation indexes of reference image quality using the best underwater image restoration, compared with the second best. Correspondingly, the Natural Image Quality Evaluator, and Natural Image Quality Measure index were improved by 2.4% and 7.4%, respectively, in the evaluation index without reference image quality. Two datasets demonstrate that the proposed restoration can be expected to treat the unnatural restoration, uneven brightness, and reddish color in the traditional underwater image restoration. The finding can also provide a technical reference for underwater image restoration.