Since capturing underwater images without degradation is challenging, there are few real image datasets with paired ground truth for underwater image enhancement. In this article, we propose a generative adversarial network (UIGAN) for underwater imaging; the network can convert images and their corresponding depth maps captured in air into images in water. The underwater imaging mechanism relies on many intrinsic parameters in water, which are difficult to estimate without field calibration. As the strong modeling capability of deep neural networks, this article uses the deep learning model to extract parameters from the real underwater environment. Then the proposed UIGAN simulates the light propagation process (direct attenuation, backscattering, and forward scattering) in water by using three modules with different constraints. We can generate a large training dataset with paired images in air and real water environment. The generated UIGAN dataset serves as input to a forward-attention transfer underwater enhancement model (FATUECNN), and it can output the restored images with appearance like those captured in air. The proposed pipeline is verified both qualitatively and quantitatively by extensive experiments and comparison evaluation with the existing state-of-the-art methods. The source code and the pre-trained model are made publicly available.

Abstract Stock investment is an important mode of asset allocation and a crucial means of financial management. How to grasp the movement of stock price and predict its trend have been the focus of investors and investment companies. Since expert stock comments contain abundant essential information for investment decisions, how to identify the topic of expert stock comments with high precision and efficiency is an important research topic. However, the existing methods usually employ single feature selection strategies for topic identification of stock comments, which may lead to low accuracy. Thus, to deal with this limitation, we propose a multi‐level information fusion method to construct a topic identification system of stock comments. Specifically, we firstly fuse various complementary feature selection methods via a multi‐view learning framework which can comprehensively represent text‐based topics. In addition, regarding the decision process, we propose a fusion strategy based on belief value which can further improve the classification performance. The experimental results indicate that the proposed multi‐level information fusion method is not only superior to other methods in terms of classification, it is also able to accurately capture topics of expert stock comments.

Underwater image restoration is one of the significant research in marine engineering and aquatic robotics. However, due to the propagation characteristics of light and the serious turbidity in underwater, the captured images often have chromatic aberration and scattering blur, which brings great challenges to the restoration of the raw image. In this paper, a revised underwater imaging model is proposed first, which reanalyzes the generation of background light from the atmosphere to the underwater and provides important support for underwater color correction. And then a network framework via the revised model is designed, which can decompose the captured image into different components corresponding to the revised model. The proposed network consists of a decomposition architecture with residual blocks that learns a complete separation of clear image and transmittance features. These two features are used along with the raw image to predict the background light. Finally, combining three constraints of the imaging model, the proposed framework can converge rapidly along the desired direction. By comparison with the performance of the state-of-the-art algorithms, the designed network shows excellent visibility and is capable of removing water on both synthetic and real-world images in different water types.

Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

The three-dimensional reconstruction from single input image is quite difficult due to many unknown parameters, such as the light conditions, the surface normal and albedo of the object. However, there are overall similar characteristics for different human faces, such as the shapes and the positions of the eyes, nose; mouth and ears are generally identical. The similar characteristics has been used in this paper to relax the numbers of the input face images, and reconstruct the 3D shape based on a couple statistical model. Moreover, the light condition of the single input image can be different from that of training database. The experiment results show the effectiveness of the proposed method.

To recover the corrupted pixels, traditional inpainting methods based on low-rank priors generally need to solve a convex optimization problem by an iterative singular value shrinkage algorithm. In this paper, we propose a simple method for image inpainting using low rank approximation, which avoids the time-consuming iterative shrinkage. Specifically, if similar patches of a corrupted image are identified and reshaped as vectors, then a patch matrix can be constructed by collecting these similar patch-vectors. Due to its columns being highly linearly correlated, this patch matrix is low-rank. Instead of using an iterative singular value shrinkage scheme, the proposed method utilizes low rank approximation with truncated singular values to derive a closed-form estimate for each patch matrix. Depending upon an observation that there exists a distinct gap in the singular spectrum of patch matrix, the rank of each patch matrix is empirically determined by a heuristic procedure. Inspired by the inpainting algorithms with component decomposition, a two-stage low rank approximation (TSLRA) scheme is designed to recover image structures and refine texture details of corrupted images. Experimental results on various inpainting tasks demonstrate that the proposed method is comparable and even superior to some state-of-the-art inpainting algorithms.

Conventional fuzzy C-means (FCM) algorithm does not consider spatial information in the clustering, which makes it sensitive to noise and inefficient. In order to overcome these problems, we propose a fast anti-noise FCM algorithm for image segmentation, which constructs a new spatial function by combining pixel gray value similarity and membership. This spatial function is used to update the membership which in turn is used to obtain the cluster centers iteratively. The proposed algorithm can achieve desirable segmentation results in less iterations and reduce the effect of noise effectively. Experimental results show that the proposed algorithm outperforms conventional FCM and other extended FCM algorithms.