As the primary means of exploring and transmitting ocean information, the acquisition and analysis of underwater video images have undoubtedly become a research hotspot for many scholars in recent years. To solve the problems of color shift, low contrast, and blurred edge details caused by absorption and scattering of light propagating underwater, researchers have clarified underwater images based on enhancement and restoration methods. By employing digital image processing techniques, enhancement-based methods improve the quality of images from the spatial domain or transform domain, such as histogram equalization, white balance, and wavelet transform. Recovery-based methods restore image clarity by solving the underwater imaging model. The main methods include improving the dark channel prior (DCP), fitting the background light scattering component, and suppressing inhomogeneous illumination. However, the above-mentioned enhancement methods do not consider the physical propagation properties of underwater light, resulting in localized over-enhancement of the images and poor subjective evaluation. The underwater imaging model adopted in the recovery method ignores the difference in transmittance between the direct attenuation component and the back scattering component, resulting in poor robustness of the model. In addition, during the parameter solution, the complex underwater environment tends to interfere with the correct estimation of the background light and transmittance parameters by these methods. Therefore, this paper builds a more robust underwater imaging model. To reduce the interference caused by the image distortion to the parameter solution, it preprocesses the original image for red channel compensation and completes the model solution through the preprocessing image, realizing the restoration of the underwater image.

Based on the traditional underwater imaging model, this paper investigates the parameter dependence of transmittance in the direct attenuation component and the back scattering component and builds a dual transmittance underwater imaging model. In solving the model, firstly, a red channel compensation algorithm is designed for preprocessing the image through the pixel correlation among the three channels to reduce the interference of color distortion to the parameter solution. Then, based on the quadtree hierarchical search algorithm, three background light candidates are obtained using smoothness, color difference, and luminance features to search, and the background light values are selected for the color channels according to the input image's luminance and edge intensity. The transmittance of the back scattering component is obtained by improving the dark channel prior and adding the saturation component refinement, and degradation-free pixel points are employed to obtain the direct component transmittance. Finally, the recovered image is obtained by inversely solving the imaging model and using histogram stretching satisfying the Rayleigh distribution to eliminate the effect of inhomogeneous illumination on imaging.

The test dataset is classified into color distorted images, fogged images, and images with artificial light by imaging scenes, and classified images as research objects. First, an ablation study is performed to verify the proposed model's validity. The results in Fig. 8 show that the dual transmittance can more accurately describe the underwater light attenuation characteristics, and the incident light attenuation term can improve the image's overall brightness and darkness difference. Then, the underwater color correction experiment of the color plate (Figs. 9 and 10) is further carried out and compared with the common underwater image restoration algorithm. Figs. 9 and 10 indicate that the proposed method accurately restores the colors of grayscale color blocks and color blocks. Finally, three sets of classified images are selected for testing (Figs. 11, 12, and 13), and the results of all methods are evaluated by UIQM, NIQE, and entropy metrics. The experimental results show that the proposed method can not only accurately correct the color distortion of the images in different scenes but also restore the detailed information more accurately, with precise edge contours.

This study builds a robust underwater imaging model with dual transmittance for the problem of color shift, blurred details, and low contrast in the images obtained in different underwater scenes. A red channel compensation algorithm is proposed to preprocess the source images based on this model which is solved through the preprocessed image to restore the underwater image. The experimental results of subjective and objective indexes show that compared with common underwater image clarification methods, the proposed method performs better in terms of color balance and more realistic detail information restoration when applied to images collected from different underwater scenes. In this paper, building the dual transmittance underwater imaging model plays a crucial role in image recovery. Before solving the model, the preprocessing means can improve the parameter estimation accuracy but will reduce the operational efficiency of the algorithm. It will be essential to work in the future on how to perform the noise reduction process for underwater images more quickly and accurately.