Two-stage algorithm for underwater image recovery for marine exploration

• Published in: Nauchno-tekhnicheskiĭ vestnik informat͡s︡ionnykh tekhnologiĭ, mekhaniki i optiki Vol. 25; no. 3; pp. 417 - 427
• Main Authors: Semernik, I. V., Samonova, Ch. V.
• Format: Journal Article
• Language: English
• Published: ITMO University 03.07.2025
• Subjects: восстановление подводных изображений; восстановление цвета; метод монте-карло; моделирование распространения света в воде; обработка подводных изображений; реальные глубоководные фотографии; улучшение подводных изображений
• ISSN: 2226-1494; 2500-0373; 2500-0373
• DOI: 10.17586/2226-1494-2025-25-3-417-427

The paper explores the problems of restoring underwater images exposed to distortions in the form of color and contrast deformations, the presence of haze, etc., arising from the interaction of optical radiation with the aquatic environment. Restoring underwater images is a non-trivial task due to the large variability of the parameters of the aquatic environment and photography conditions. The proposed method, unlike other underwater image recovery algorithms based on an imaging model, is not based on a simplified exponential Beer-Lambert law for estimating optical radiation attenuation in water, but on a more accurate physical approach that simulates the propagation of optical rays in water using the Monte Carlo method, taking into account the main parameters the water environment and the camera. The results of numerical simulation of optical ray propagation in an aquatic environment are used for image processing in the spatial domain by editing the histograms of each image channel in the RGB color space. To test the developed algorithm, 6 real underwater images were selected obtained under various lighting conditions (natural and artificial) and various parameters of the aquatic environment (clear ocean and turbid coastal water). For the purpose of qualitative and quantitative analysis of the obtained results, the following similar underwater image processing methods were used: Fusion, UDCP IATP, Retinex, HE, and UWB VCSE. The Underwater Colour Image Quality Evaluation Metric (UCIQE) and Underwater Image Quality Measure (UIQM) indicators were used to quantify the results obtained. The results of the qualitative assessment demonstrate the high efficiency of the proposed method: regardless of the conditions of the initial image parameters, the application of the developed method improves visual perception and does not lead to excessive contrast enhancement, color distortion, loss of detail, the appearance of artifacts, etc. Quantification of underwater image processing results demonstrates comparable and superior results when comparing the efficiency of the algorithm with similar methods. For the UCIQE parameter, the developed method provided an improvement from 9 % to 51 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 82 %. For the UIQM parameter, the developed method provided an improvement from 24 % to 99 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 123 %. Unlike analogues, the developed method did not demonstrate the worst values of the UCIQE and UIQM parameters for any processed image, which indicates the stability of the method regardless of the parameters of the aquatic environment and shooting conditions. By dividing the developed method into preliminary and main stages, high image processing speed is ensured: 0.073 seconds for images with a resolution of 400 × 300 pixels and from 8.02 to 8.23 seconds for images with a resolution of 5184 × 3456 pixels. Similar methods demonstrated values from 0.19 to 10.81 seconds for an image with a resolution of 400 × 300 pixels and from 7.65 to 937.83 seconds for an image with a resolution of 5184 × 3456 pixels. The introduction of the proposed method into the geological exploration will increase their efficiency and reliability, and will provide more accurate data for further exploration of solid mineral deposits. Such technique integrated into the machine vision system of underwater vehicles will significantly expand their functionality by enabling automation of operations and improving the efficiency of recognition systems.