Underwater active polarization descattering based on a single polarized image

Restoration of Turbid Underwater Images of Cobalt Crusts Using Combined Homomorphic Filtering and a Polarization Imaging System

Marine cobalt-rich crusts, extensively used in industries such as aerospace, automotive, and electronics, are crucial mineral resources located on the ocean floor. To effectively exploit these valuable resources, underwater imaging is essential for real-time detection and distribution mapping in mining areas. However, the presence of suspended particles in the seabed mining environment severely degrades image quality due to light scattering and absorption, hindering the effective identification of the target objects. Traditional image processing techniques-including spatial and frequency domain methods-are ineffective in addressing the interference caused by suspended particles and offer only limited enhancement effects. This paper proposes a novel underwater image restoration method that combines polarization imaging and homomorphic filtering. By exploiting the differences in polarization characteristics between suspended particles and target objects, polarization imaging is used to separate backscattered light from the target signal, enhancing the clarity of the cobalt crust images. Homomorphic filtering is then applied to improve the intensity distribution and contrast of the orthogonal polarization images. To optimize the parameters, a genetic algorithm is used with image quality evaluation indices as the fitness function. The proposed method was compared with traditional image processing techniques and classical polarization imaging methods. Experimental results demonstrate that the proposed approach more effectively suppresses backscattered light, enhancing the clarity of target object features. With significant improvements in image quality confirmed by several no-reference quality metrics, the method shows promise as a solution for high-quality underwater imaging in turbid environments, particularly for deep-sea mining of cobalt-rich crusts.

Effect of the estimation result of the degree of polarization of target light on clear imaging

Previous underwater imaging methods have not developed a clear idea of estimating the degree of polarization of target light (Pobj). To address this issue, this Letter answers the question of how the estimation result of Pobj affects clear imaging. First, the theoretical derivation states that Pobj is simply a scale modulation factor of the imaging result. Second, experiments are conducted for validation, and results conform well to the derivation. Hence, the effect of the estimated Pobj on clear imaging is obtained. This parameter only influences brightness rather than contrast but may cause noise amplification as well as the unfavorable result of negative pixels. Therefore, no precise estimation is needed; pick the value near the ends of the definition domain directly and take the absolute value. Based on these, a new imaging formula is proposed, enabling the processing time to fulfill the actual dynamic imaging requirements. As far as we are concerned, the attained prior knowledge and formula could provide strong assistance for underwater polarization imaging.

Image recovery method for underwater targets with complex polarization characteristics

Polarization imaging techniques have been effective in improving the clarity of turbid underwater images affected by water scattering. These techniques offer valuable additional information compared to traditional methods. However, previous descattering methods have mostly been designed for targets with uniform distribution of polarimetric characteristics. Therefore, targets with complex polarization characteristics have non-uniform distribution of polarization characteristics when dealing with such problems, additional parameter estimation errors can limit the results of image recovery. This paper proposes what we believe is a novel approach to address this issue. The method involves obtaining a new set of images using the polarization images vector space transformation method. The angle of polarization (AOP) of the target reflected light is estimated using the additivity law of the Stokes vector. This information is then combined with the physical model of underwater imaging to recover the underwater images affected by scattering. Experimental results conducted under varying levels of water turbidity validate the effectiveness of the proposed method. Moreover, the proposed method significantly broadens the range of application scenarios.

Underwater dynamic polarization imaging without dependence on the background region

Active-polarization imaging holds significant promise for achieving clear underwater vision. However, only static targets were considered in previous studies, and a background region was required for image restoration. To address these issues, this study proposes an underwater dynamic polarization imaging method based on image pyramid decomposition and reconstruction. During the decomposition process, the polarized image is downsampled to generate an image pyramid. Subsequently, the spatial distribution of the polarization characteristics of the backscattered light is reconstructed by upsampling, which recovered the clear scene. The proposed method avoids dependence on the background region and is suitable for moving targets with varying polarization properties. The experimental results demonstrate effective elimination of backscattered light while sufficiently preserving the target details. In particular, for dynamic targets, processing times that fulfill practical requirements and yield superior recovery effects are simultaneously obtained.

High-throughput microplastic assessment using polarization holographic imaging

Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.