Underwater images require further enhancement to improve the image qualities caused by medium scattering and light absorption. Based on Contour Bougie (CB) morphology, we propose a new enhancement method to enhance the scene contours and improve the visibility of images captured underwater. Two structuring elements with different sizes are considered as the roving windows. Multiple morphological operations are designed for highlighting the rich details on the origin images. The enhanced images are normalized and stretched to improve the white balance of RGB channels. The comprehensive study of state-of-the-art algorithms is conducted to interpret the improvement of image quality by the proposed method. In addition, we use 890 raw underwater degraded images as the testing data. The quantitative and qualitative evaluations of these data demonstrate that the proposed method achieves better visible contrast for highlighting the details of the undersea creatures. The comparison with different underwater scenes proves that the proposed method improves the color balances of the degraded images.

Real-world underwater images suffer from quality degeneration caused by the scattering and absorption of light propagation. The damage of the detailed textures in underwater images shows the negative effect of detection and recognition. To recovery the image visibility and sharpness for the above applications, a new image enhancement method is proposed for extracting the image textures. To enhance the image textures with high quality, we propose a multiscale fusion enhancement. Two new fusion inputs are built on different color methods. One input is devoted to improve the sharpness by contrast-based dark channel prior dehazing in the red–green–blue (RGB) model. The other input is designed based on multiple morphological operation and color compensation from the opponent color in the CIE $1976~L^{\ast}a^{\ast}b^{\ast}$ color space (CIELAB) model. This input is used to enhance the counter brightness and adjust the color distribution. The dominant features of the two inputs are merged. Therefore, the contrast of the fusion output is enhanced adaptively to recover the final enhanced result. Compared with the state-of-the-art methods, our results reveal that the proposed method can enrich the image textures based on an impressive visual perception of contrast, saturation, and sharpness. Moreover, our method also shows strong robustness in challenge scenes and improves the performance of several underwater applications.

Vision-guided autonomous underwater vehicles based on remote sensing play an important role in ocean missions. However, some problems exist in underwater visual perception, such as color distortion, low contrast, and fuzzy details, which restrict the applications of underwater visual tasks. Most of the state-of-the-art image enhancement methods are still limited in scene adaptability, recovery accuracy, and real-time processing. To solve these problems, we propose an underwater sensing scene image enhancement method called a multiscale feature fusion network (MFFN). To extract the multiscale feature, the measure merging the feature extraction module, the feature fusion module, and the attention reconstruction module is designed. This measure can also enhance the adaptability and visual effect of the scene. Moreover, we propose multiple objective functions for supervised training to match the nonlinear mapping. Based on the qualitative and quantitative evaluations, the proposed method produces competitive performance compared with some state-of-the-art methods, and the perception and statistical quality of underwater images are enhanced effectively.

Underwater detection is a crucial task to lay the foundation for the intelligent marine industry. In contrast to land scenes, targets in degraded underwater environments show ambiguous and surrounding-similar profiles, causing it challenging for generic detectors to accurately extract features. Eliminating the interference of ambiguous features is one of the primary goals when recognizing underwater objects against complex backgrounds. To this aim, we propose a novel detection framework called underwater distraction mining detector (UDMDet). UDMDet is an end-to-end detector and has two key modules: distraction-aware FPN (DAFPN) and task-aligned head (THead). DAFPN is designed to progressively refine the coarse features via mining the discrepancies between objects and backgrounds, while THead enhances the information interaction between classification and localization to make predictions with higher quality. To overcome the feature ambiguous problem, the underwater distraction-aware model is proposed to extract the differences between objects and surroundings so as to clear the target boundary. Experimental results show that UDMDet can more effectively discover objects conceal on real-world underwater images and has a higher precision outperforming the state-of-the-art detectors.

The influence of rocks on the microwave thermal emission (MTE) of the lunar regolith has not been fully studied with the four-channel microwave radiometer (MRM) data onboard Chang’e-1/2 satellites. To highlight the influence of the rocks on the MTE of the regolith, the Hertzsprung basin located near the lunar equator in highland regions is selected as the study area. The comparison between the brightness temperature (TB) maps derived from the Chang’e-2 MRM data and rock abundance (RA) map derived from the Diviner data postulates three special issues about the correlation between the MTE features and the regolith with rocks. Then, aimed to interpret the issues, two new layered regolith models and the corresponding radiative transfer models are constructed. The main results are as follows. First, the observation and the simulation both verify that the regolith with rocks will provide a cold TB anomaly at night and at low frequencies at daytime, but result in a hot anomaly at high-frequency at daytime. Moreover, the temperature profiles of the regolith with surface and hidden rocks are evaluated with the theoretical model. Second, the simulation results verify the existence of the hidden rocks in the lunar regolith assumed when studying the TB performances of the Hertzsprung basin. Third, the rock distribution revealed by the TB maps shows a different view compared to that estimated by the Diviner data in space and values, and the change of the TB with frequencies postulates a new view about the variation of the RA with depth. This study hints that the MRM data probably provide a new way to quantitatively estimate the RA values of the lunar regolith, and the results will be meaningful to improve understanding of the evolution of the impact craters.