Scene recovery is a fundamental imaging task with several practical applications, including video surveillance and autonomous vehicles, etc. In this paper, we provide a new real-time scene recovery framework to restore degraded images under different weather/imaging conditions, such as underwater, sand dust and haze. A degraded image can actually be seen as a superimposition of a clear image with the same color imaging environment (underwater, sand or haze, etc.). Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity $O(N)$ is derived for real-time recovery. For general cases, we develop ROP $^+$ to further improve the recovery performance. Comprehensive experiments of the scene recovery illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness.

Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image and the clean one, our method is built on the surface-aware strategy arising from the intrinsic geometrical consideration. This approach facilitates the blur kernel estimation due to the preserved sharp edges in the intermediate latent image. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on deblurring the text and natural images. Moreover, our method can achieve attractive results in some challenging cases, such as low-illumination images with large saturated regions and impulse noise. A direct extension of our method to the non-uniform deblurring problem also validates the effectiveness of the surface-aware prior.

The transform-based tensor nuclear norm (TNN) methods have recently yielded promising results for tensor completion. The primary goal of these methods is to exploit the low-rank structure of frontal slices under the transform along the third mode. However, these methods typically neglect that the third mode fiber of a tensor is a one-dimensional signal, which is sparse under some transforms. In this study, we suggest a collaborative sparse and low-rank transforms model called CSLRT for third-order tensor completion. We simultaneously exploit the sparsity of third mode fibers and the low-rankness of frontal slices under learned transforms. In our work, the transformed sparsity is complementary to the transformed low-rankness, and they are organically combined and benefit from each other. Moreover, for tensors with limited correlation along the third mode (e.g., color images), we suggest three-directional CSLRT (3DCSLRT) to fully explore the transformed sparsity of fibers and transformed low-rankness of slices along three modes. To tackle proposed models, we develop multi-block proximal alternating minimization (PAM) algorithms and establish the theoretical guarantee. Extensive experimental results on real-world data demonstrate that proposed methods outperforms state-of-the-art competitors qualitatively and quantitatively.

Communities are often associated with important structural characteristics of a complex network system, therefore detecting communities is considered to be a fundamental problem in network analysis. With the development of data collection technology and platform, more and more sources of network data are acquired, which makes the form of network as well as the related data more complex. To achieve integrative community detection of a multi-layer attributed network that involves multiple network layers together with their attribute data, effectively utilizing the information from the multiple networks and the attributes may greatly enhance the accuracy of community detection. To this end, in this article, we study the integrative community detection problem of a multi-layer attributed network from the perspective of matrix factorization, and propose a penalized alternative factorization (PAF) algorithm to resolve the corresponding optimization problem, followed by the convergence analysis of the PAF algorithm. Results of the numerical study, as well as an empirical analysis, demonstrate the advantages of the PAF algorithm in community discovery accuracy and compatibility with multiple types of network-related data.

Pansharpening is an image fusion process aiming to generate high-resolution multispectral (HRMS) images from a pair of low-resolution multispectral (LRMS) images and a high-resolution PAN image. It is a fundamental and significant task for the widespread use of remote sensing images. This paper proposes a new residual learning-based multispectral pansharpening network constrained by two deep physical models, collectively termed as P3Net. It mainly consists of the mainstream PDFNet and the other two auxiliary physical models, M2PNet and H2LNet. Unlike the existing methods of processing only one image scale, the proposed PDFNet fully extracts the spatial details from the multi-level image pyramid with decreasing spatial scales. Then, the spatial information is injected into the upsampled LRMS image. Since the pan-sharpened result should be consistent with the observed inputs under the physics models, we learn deep pansharpening physics models to reflect the inverse relationships. In detail, we propose the lightweight M2PNet and H2LNet to represent the latent non-linear mappings from the HRMS image to the panchromatic (PAN) image and the LRMS image, respectively. The two pre-trained physics models are frozen and guide the training of the PDFNet, so as to drive clear physical interpretability and further suppress the spectral and spatial distortions. The comparative experiments with the existing state-of-the-art pansharpening methods on QuickBird, GaoFen, and WorldView test sets demonstrate the superiority of the proposed method in terms of both quantitative metrics and subjective visual effects. The codes are available at https://github.com/KSJhon/PyramidPanWithPhysics.