Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such as noises (random noise), blurs (Gaussian and uniform blur), and downsampled (both spectral and spatial downsample), each corresponding to the HSI denoising, deblurring, and super-resolution tasks, respectively. Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit the structurally spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this article, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which nonlocal similarity within spectral-spatial cubic and spectral correlation are simultaneously captured by third-order tensors. Furthermore, to improve the capability and flexibility, we formulate it as a weighted low-rank tensor recovery (WLRTR) model by treating the singular values differently. We demonstrate the reweighed strategy, which has been extensively studied in the matrix, also greatly benefits the tensor modeling. We also consider the stripe noise in HSI as the sparse error by extending WLRTR to robust principal component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed WLRTR models consistently outperform state-of-the-art methods in typical HSI low-level vision tasks, including denoising, destriping, deblurring, and super-resolution.

Most of existing medical image denoising methods focus on estimating either the image or the residual noise. Moreover, they are usually designed for one specific noise with a strong assumption of the noise distribution. However, not only the random independent Gaussian or speckle noise but also the structurally correlated ring or stripe noise, are ubiquitous in various medical imaging instruments. Explicitly modeling the distributions of these complex noises in the medical image is extremely hard. They cannot be accurately held by the Gaussian or mixture of Gaussian model. To overcome the two drawbacks, in this work, we propose to treat the image and noise components equally and convert the image denoising task into an image decomposition problem naturally. More precisely, we present a two-stage deep convolutional neural network (CNN) to model both the noise and the medical image simultaneously. On the one hand, we utilize both the image and noise to separate them better. On the other hand, the noise sub-network serves as a noise estimator which guides the image sub-network with sufficient information about the noise, thus we could easily handle different noise distributions and noise levels. To better cope with the gradient vanishing problem in this very deep network, we introduce both the short-term and long-term connections in the network which could promote the information propagation between different layers efficiently. Extensive experiments have been performed on several kinds of medical noise images, such as the computed tomography and ultrasound images, and the proposed method has consistently outperformed state-of-the-art denoising methods.

This article presents a visual navigation and landing control paradigm for an unmanned aerial vehicle (UAV) to land on a moving autonomous surface vehicle (ASV). Therein, an adaptive learning navigation rule with a multilayer nested guidance is designed to pinpoint the position of the ASV and to guide and control the UAV to fulfill horizontal tracking and vertical descending in a narrow landing region of the ASV by means of merely relative position feedback. To ensure the feasibility of the proposed control law, asymptotical stability conditions are derived based on Lyapunov stability theory. Landing experimental results are reported for a UAV-ASV system consisting of an M-100 UAV and a self-developed three-meters-long HUSTER-30 ASV on a lake to substantiate the efficacy of the proposed landing control method.

Detecting infrared dim and small targets is one crucial step for many tasks such as early warning. It remains a continuing challenge since characteristics of infrared small targets, usually represented by only a few pixels, are generally not salient. Despite that many traditional methods have significantly advanced the community, their robustness or efficiency is still lacking. Most recently, CNN-based object detection has achieved remarkable performance and some researchers focus on it. However, these methods are not computationally efficient when implemented on some CPU-only machines and few datasets are available publicly. To promote the detection of infrared small targets in complex backgrounds, we propose a new lightweight CNN-based architecture. The network contains three modules: the feature extraction module is designed for representing multi-scale and multi-level features, the grid resample operation module is proposed to fuse features from all scales, and a decoupled head to distinguish infrared small targets from backgrounds. Moreover, we collect a brand-new infrared small target detection dedicated dataset which consists of 68311 practical captured images with complex backgrounds for alleviating the data dilemma. To validate the proposed model, 54758 images are used for training and 13553 images are used for testing respectively. Extensive experimental results demonstrate that the proposed method outperforms all traditional methods by a large margin and runs much faster than other CNN methods with high precision. The proposed model can be implemented on the Intel i7-10850H CPU (2.3GHz) platform and Jetson Nano for real-time infrared small target detection at 44 FPS and 27 FPS, respectively. It can be even deployed on an Atom x5-Z8500 (1.44GHz) machine at about 25 FPS with 128×128 local images. The source codes and the dataset have been made publicly available at https://github.com/SeaHifly/Infrared-Small-Target.

Detecting oriented objects along with estimating their rotation information is one crucial step for image analysis, especially for remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g., the rotation angle) or a few (e.g., several coordinates) groundtruth (GT) values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To this end, we propose a mechanism that simultaneously learns the regression of horizontal proposals, oriented proposals, and rotation angles of objects in a consistent manner, via naive geometric computing, as one additional steady constraint . An oriented center prior guided label assignment strategy is proposed for further enhancing the quality of proposals, yielding better performance. Extensive experiments on six datasets demonstrate the model equipped with our idea significantly outperforms the baseline by a large margin and several new state-of-the-art results are achieved without any extra computational burden during inference. Our proposed idea is simple and intuitive that can be readily implemented. Source codes are publicly available at: https://github.com/wangWilson/CGCDet.git.