MINM: Marine intelligent netting monitoring using multi-scattering model and multi-space transformation

Marine intelligent net tank aquaculture monitoring plays an important role in improving aquaculture efficiency, environmental monitoring efficiency, and environmental safety. The underwater environment has complex light, often with problems such as scattering and absorption, resulting in poor image quality, making it difficult to accurately analyze and judge the aquaculture environment. Improving marine intelligent net tank aquaculture monitoring has the following three advantages: 1) better observation and monitoring of the aquaculture process, timely detection of problems and abnormalities, to protect the benefits of aquaculture and product quality. 2) more convenient and rapid monitoring of the aquaculture environment, improving monitoring efficiency and reducing monitoring costs. 3) effective monitoring of the underwater environment around the farm, and timely detection of foreign pollution, harmful substances, and other problems, to protect the safety of the aquaculture environment. Therefore, in order to solve the two degradation problems of scattering and absorption in the process of marine smart net farm monitoring, we propose a marine smart net farm monitoring method using multiple scattering models and multiple spatial transformations, called MINM. Specifically, inspired by the image chromatic aberration correction method, we design a color correction method in the multicolor space, which is implemented by using the Lab and RGB color space by performing contrast-constrained adaptive histogram equalization and gray world assumptions, respectively, to correct color shifts in different color models. Based on this, we propose a de-scattering method using a multi-scattering model, which eliminates the effect of scattering on underwater imaging by embedding a complete multi-scattering underwater imaging model to guide the extraction of different features in the multi-scattering model. To obtain more qualified results, we also propose an efficient perceptual fusion to mix the output of the de-scattering and color correction. Thus, our method can take advantage of multiple scattering models and multiple spatial transformations to effectively improve the visual quality of underwater images, producing enhanced results that fit the complete underwater imaging model and have bio-visual characteristics. In extensive experimental demonstrations, our MINM method has shown higher performance than the state-of-the-art methods in terms of both visual quality and quantitative metrics. All experimental results and datasets in this paper are available from the following website: https://github.com/An-Shunmin/MINM.