Due to light scatter and absorption in waterbody, underwater imaging can be easily impaired with low contrast and visual distortion. The resulting images are often unable to meet the quality requirements of human perception and computer processing. Therefore, Underwater Image Enhancement (UIE) has been attracting extensive research efforts. Although deep learning has demonstrated its great success in many vision tasks, its huge amounts of parameters and computations are not conducive to UIE in resource-limited scenarios. In this paper, we address this issue by proposing a Lightweight Cascaded Network (LCNet) based on Laplacian image pyramids. At each pyramid level, we implement cascaded blocks upon a residual network. Specifically, high quality residuals can be progressively predicted with significantly reduced complexity in a coarse-to-fine fashion. Furthermore, these sub-networks are recursively nested to build our LCNet, thereby reducing the overall computational complexity with reused parameters. Extensive experiments demonstrate that the proposed method performs favorably against the state-of-the-arts in terms of visual quality, model parameters and complexity.

Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images in CIELab space, we present the naturalness, sharpness, and structure indexes. Among them, the naturalness and sharpness indexes represent the visual improvements of enhanced images; the structure index indicates the structural similarity between the underwater images before and after UIE. We combine all indexes with a saliency-based spatial pooling and thus obtain the final UIF metric. To evaluate the proposed metric, we also establish a first-of-its-kind large-scale UIE database with subjective scores, namely Underwater Image Enhancement Database (UIED). Experimental results confirm that the proposed UIF metric outperforms a variety of underwater and general-purpose image quality metrics. The database and source code are available at https://github.com/z21110008/UIF .

Video surveillance systems are used to enhance the public safety and private assets. Automatic anomaly detection is vital in such surveillance systems to reduce the human labor and its associated costs. Previous works only consider spatial-temporal features. In many complex real-world scenarios, such visual features are unable to capture the semantic meanings required to further improve accuracy. To deal with such issues, we propose a novel framework: Text Empowered Video Anomaly Detection (TEVAD) which utilizes both visual and text features. Text features complements the visual features as they are semantically rich. Specifically, we compute text features based on the captions of the videos to capture the semantic meanings of abnormal events and thus improve the overall performance of video anomaly detection. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art results on four benchmark datasets (i.e. ShanghaiTech, UCF-Crime, XD-Violence, and UCSD-Pedestrians) and achieves improved robustness. We further analyze the captions to provide additional explainability for the anomalous videos identified by our proposed algorithm. Our codes are available at https://github.com/coranholmes/TEVAD.

Underwater imaging is usually degraded by low contrast or color cast due to light absorption and scattering. This fact limits the accuracy of object recognition in underwater and marine environments. In this article, to address this issue, we propose a CURE-Net that progressively improves degraded underwater images in a coarse-to-fine way. To be specific, our CURE-Net consists of three cascaded subnetworks. The first two subnetworks use attention and gate fusion to learn multiscale contextual information, whereas the third subnetwork preserves fine spatial details. A detail enhancement block and a supervised recovery block are deployed between subnetworks to further improve the recovery of colors and details. Our proposed CURE-Net is extensively evaluated on popular data sets and achieves state-of-the-art performance. Compared with its peers, our method is also more friendly to object recognition—underwater objects are more easily recognized after being processed by our method.

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time. Codes and models are available at https://github.com/StephenLinn/LMQFormer .

Sonar imagery plays a significant role in oceanic applications since there is little natural light underwater, and light is irrelevant to sonar imaging. Sonar images are very likely to be affected by various distortions during the process of transmission via the underwater acoustic channel for further analysis. At the receiving end, the reference image is unavailable due to the complex and changing underwater environment and our unfamiliarity with it. To the best of our knowledge, one of the important usages of sonar images is target recognition on the basis of contour information. The contour degradation degree for a sonar image is relevant to the distortions contained in it. To this end, we developed a new no-reference contour degradation measurement for perceiving the quality of sonar images. The sparsities of a series of transform coefficient matrices, which are descriptive of contour information, are first extracted as features from the frequency and spatial domains. The contour degradation degree for a sonar image is then measured by calculating the ratios of extracted features before and after filtering this sonar image. Finally, a bootstrap aggregating (bagging)-based support vector regression module is learned to capture the relationship between the contour degradation degree and the sonar image quality. The results of experiments validate that the proposed metric is competitive with the state-of-the-art reference-based quality metrics and outperforms the latest reference-free competitors.