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.

Stripe noise from different remote sensing imaging systems varies considerably in terms of response, length, angle, and periodicity. Due to the complex distributions of different stripes, the destriping results of previous methods may be oversmoothed or contain residual stripe. To overcome this key problem, we provide a comprehensive analysis of existing destriping methods and propose a deep convolutional neural network (CNN) for handling various kinds of stripes. Moreover, previous methods individually model the stripe or the image priors, which may lose the relationship between them. In this article, a two-stream CNN is designed to simultaneously model the stripe and image, which better facilitates distinguishing them from each other. Moreover, we incorporate the wavelet into our CNN model for better directional feature representation. Therefore, the CNN learns the discriminative representation from the external data set, while the wavelet models the internal directionality of the stripe, in which both the internal and external priors are beneficial to the destriping task. In addition, the wavelet extracts the multiscale information with a larger receptive field for global contextual information modeling; thus, we can better distinguish the stripe from the similar image line pattern structures. The proposed method has been extensively evaluated on a number of data sets and outperforms the state-of-the-art methods by substantially a large margin in terms of quantitative and qualitative assessments, speed, and robustness.

Recently, transform-based tensor nuclear norm (TNN) minimization methods have received increasing attention for recovering third-order tensors in multi-dimensional imaging problems. The main idea of these methods is to perform the linear transform along the third mode of third-order tensors and then minimize the nuclear norm of frontal slices of the transformed tensor. The main aim of this paper is to propose a nonlinear multilayer neural network to learn a nonlinear transform by solely using the observed tensor in a self-supervised manner. The proposed network makes use of the low-rank representation of the transformed tensor and data-fitting between the observed tensor and the reconstructed tensor to learn the nonlinear transform. Extensive experimental results on different data and different tasks including tensor completion, background subtraction, robust tensor completion, and snapshot compressive imaging demonstrate the superior performance of the proposed method over state-of-the-art methods.

In the infrared focal plane arrays imaging systems, the temperature-dependent nonuniformity effects severely degrade the image quality. In this letter, we propose a very deep convolutional neural network for unified infrared aerothermal nonuniform correction. Our network is built with the multiscale and residual training. The multiscale subnetworks utilize the multiscale property in the images, and the long-short-term residual learning contributes to the information propagation. Compared with the previous methods, the proposed method is more robust to various nonuniform artifacts and more efficient at processing time. Experimental results validate the superiority of our method for infrared nonuniform correction.

Unmanned aerial vehicles (UAVs) have been widely applied in military and civilian fields, but they also pose great threats to restricted areas, such as densely populated areas and airports. Thermal infrared (IR) imaging technology is capable of monitoring UAVs at a long range in both day and night conditions. Therefore, the anti-UAV technology based on thermal IR imaging has attracted growing attention. However, the images acquired by IR sensors often suffer from small and dim targets, as well as heavy background clutter and noise. Conventional detection methods usually have a high false alarm rate and low detection accuracy. This article proposes a detection method that formulates the UAV detection as predicting the residual image (i.e., background, clutter, and noise) by learning the nonlinear mapping from the input image to the residual image. The UAV target image is obtained by subtracting the residual image from the input IR image. The constructed end-to-end U-shaped network exploits the depthwise separable residual dense blocks in the encoder stage to extract the abundant hierarchical features. Besides, the multiscale feature fusion and representation block is introduced to fully aggregate multiscale features from the encoder layers and intermediate connection layers at the same scale, as well as the decoder layers at different scales, to better reconstruct the residual image in the decoder stage. In addition, the global residual connection is adopted in the proposed network to provide long-distance information compensation and promote gradient backpropagation, which further improves the performance in reconstructing the image. The experimental results show that the proposed method achieves favorable detection performance in real-world IR images and outperforms other state-of-the-art methods in terms of quantitative and qualitative evaluation metrics.