In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-agent GAIL (MAG) algorithm is proposed. The GAIL enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network. Parallel training of multi-agents reduces the high correlation between samples to avoid local convergence. In addition, a reward function is designed to help training. Finally, the results show that in the unity simulation platform test, the proposed algorithm has a strong optimal decision-making ability in the tracking process.

In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance.

Deep-learning-based methods have been widely used in hyperspectral image classification. In order to solve the problems of the excessive parameters and computational cost of 3D convolution, and loss of detailed information due to the excessive increase in the receptive field in pursuit of multi-scale features, this paper proposes a lightweight hybrid convolutional network called the 3D lightweight receptive control network (LRCNet). The proposed network consists of a 3D depthwise separable convolutional network and a receptive field control network. The 3D depthwise separable convolutional network uses the depthwise separable technique to capture the joint features of spatial and spectral dimensions while reducing the number of computational parameters. The receptive field control network ensures the extraction of hyperspectral image (HSI) details by controlling the convolution kernel. In order to verify the validity of the proposed method, we test the classification accuracy of the LRCNet based on three public datasets, which exceeds 99.50% The results show that compare with state-of-the-art methods, the proposed network has competitive classification performance.

This paper aims to study the application of hyperspectral technology in the classification of deep-sea manganese nodules. Considering the spectral spatial variation of hyperspectral images, the difficulty of label acquisition, and the inability to guarantee stable illumination in deep-sea environments. This paper proposes a local binary pattern manifold superpixel-based fuzzy clustering method (LMSLIC-FCM). Firstly, we introduce a uniform local binary pattern (ULBP) to design a superpixel algorithm (LMSLIC) that is insensitive to illumination and has texture perception. Secondly, the weighted feature and the mean feature are fused as the representative features of superpixels. Finally, it is fused with fuzzy clustering method (FCM) to obtain a superpixel-based clustering algorithm LMSLIC-FCM. To verify the feasibility of LMSLIC-FCM on deep-sea manganese nodule data, the experiments were conducted on three different types of manganese nodule data. The average identification rate of LMSLIC-FCM reached 83.8%, and the average true positive rate reached 93.3%, which was preferable to the previous algorithms. Therefore, LMSLIC-FCM is effective in the classification of manganese nodules.