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Due to the scattering and attenuation of light into the water, the underwater image usually appears with color distortion, blurred details, and low contrast. To address these problems, a novel two-stage underwater image convolutional neural network (CNN) based on structure decomposition (UWCNN-SD) for underwater image enhancement is proposed by considering the characteristics of underwater imaging. Specifically, the raw underwater image is decomposed into high-frequency and low-frequency based on theoretical analysis of the underwater imaging. Then, a two-stage underwater enhancement network including a preliminary enhancement network and a refinement network is proposed. In the first stage, the preliminary enhancement network, which contains the high-frequency and the low-frequency enhancement networks, is proposed. The high-frequency part is enhanced directly by a deep learning network, and the low-frequency enhancement network is based on the underwater imaging, which is integrated transmission map and background light into joint component map. In the second stage, the refinement network is designed to further optimize the color of the underwater image by considering complexity of underwater imaging. The experimental results of synthetic and real-world underwater images/videos demonstrate that the proposed UWCNN-SD method can perform color correction and enhancement on different types of underwater images. The ablation study verifies the effectiveness of each component, and application tests further illustrate that the proposed UWCNN-SD method can obtain underwater images with higher visual quality. The trained model is available at: https://github.com/wushengcong/UWCNN-SD .

The previous studies on service resource scheduling for maintenance, repair and operation (MRO) ignore the diverse service processes from different complex equipment and the coordination of task sc...

The friction between a molybdenum disulphide (MoS2) nanoflake and a MoS2 substrate was analyzed using a modified Tomlinson model based on atomistic force fields. The calculations performed in the study suggest that large deformations in the substrate can induce a dramatic decrease in the friction between the nanoflake and the substrate to produce the so-called superlubricity. The coefficient of friction decreases by 1–4 orders of magnitude when a high strain exceeding 0.1 is applied. This friction reduction is strongly anisotropic. For example, the reduction is most pronounced in the compressive regime when the nanoflake slides along the zigzag crystalline direction of the substrate. In other sliding directions, the coefficient of friction will reduce to its lowest value either when a high tensile strain is applied along the zigzag direction or when a high compressive strain is applied along the armchair direction. This anisotropy is correlated with the atomic configurations of MoS2.

In recent years, enhancement of underwater images based on generative adversarial network (GAN) has been widely used. Aiming at the shortcoming that the texture details of the images generated by GAN are not clear enough, an underwater image pre-process unit and a generator including a perceptual subnet and a refine subnet are designed to improve visual quality of underwater images. The role of the perceptual subnet is to maintain the structure and semantic information of the input image. Refine subnet is to make the generated image clearer in the details of the texture. Specially, considering that underwater images may prevent the network from learning effectively, an underwater image pre-process unit is proposed to improve the quality of underwater images. To maintain the structure and semantic information of underwater images and preserve texture details of underwater images, the perceptual subnet and the refine subnet are proposed, respectively. Then, color-structure perception loss is proposed to obtain good performance in color and structure, and content loss and detail loss are proposed to keep content consistent and sharper texture. Ablation study verify the rationality and effectiveness of each loss function. Finally, subjective and objective experiments prove that the proposed method can obtain underwater images with higher visual quality and clearer texture details.