Gaining accurate channel state information (CSI) through a low-cost scheme has always been difficult in wireless communication systems. One of the current research directions is to obtain the CSI from the channel knowledge map (CKM) based on the users’ location. However, the direct utilization of CSI in CKM is hindered due to the sensitivity of instantaneous CSI to time-varying scattering environments and positioning errors. To address this issue, this paper proposes a channel prediction scheme that combines the CKM with historical user CSI to enhance the beamforming performance in multiple-input multiple-output (MIMO) systems. Specifically, the joint-orthogonal matching pursuit algorithm is used to accurately reconstruct the user channel with high precision using a limited number of pilots, and the multi-path components tracking algorithm is employed to extract the common and independent support sets of paths from the estimated channel and the CKM. Lastly, an adaptive and low-complexity predictor is utilized to obtain the future user CSI. The proposed scheme has been evaluated using multiple measured channel datasets, the results indicate a significant improvement in predicting channel cosine similarity compared to directly using the CSI from CKM and existing schemes.

Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the proposed approach has superior results in terms of quantitative performance metrics and the preservation of intricate visual details, compared to several well-known SAR image despeckling methods.

Tensor completion has attracted increasing attention in signal processing, computer vision, and biomedical engineering. By using nuclear norm minimization, a tensor completion problem can be converted into a convex program and enjoys properties gained from matrix completion. The low rank property has been widely used for tensor/matrix completion. However, the prior subspace information can also be utilized, which has been ignored and does not exhibit its full power in the existing formulation. In this paper, we propose a new framework leveraging tensor subspace prior for the sum of nuclear norm (SNN) minimization, which supports a range of tensor decompositions. By using the knowledge of the self-prior (SP)/nonself-prior (NSP) and further designing an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM), the performance of tensor completion can be enhanced. The superiority of the proposed method is verified by extensive numerical experiments.

Hybrid precoding is considered in wideband mm-Wave massive MIMO-OFDM systems with beam squint. Traditional wideband hybrid precoding schemes cannot achieve near-optimal sum rate as digital precoding/beamforming (DBF) and may induce high hardware cost. Dynamic time-domain weighting hybrid precoding (DTW-HBF) updates the analog weights during an OFDM symbol, realizing equivalent frequency-dependent analog precoding and approximating DBF with a low cost. Directly reconstructing the time-domain signals, however, involves pseudo-inverse operations, which may cause numerical instability. In this work, the frequency-domain spectrum is reconstructed by introducing the optimal frequency-domain analog precoder and using the cyclic convolution property of Discrete Fourier Transform (DFT). The proposed method can approximately approach the performance of DBF while maintaining the hardware structure based on phase shifters (PSs). As shown by simulation results, an increased sum rate has been achieved.

Generative diffusion models have recently shown impressive results in image restoration. However, the predicted noise from existing diffusion-based methods may be inaccurate, especially when the noise amplitude is small, thereby leading to sub-optimal results. In this letter, an unsupervised diffusion model with latent low-rank regularization is proposed to alleviate this challenge. In particular, we first create a latent low-rank space using self-supervised learning for each degraded images, from which we derive corresponding latent low-rank regularization. This regularization, combining with observed prior information and smoothness regularization, guides the reserve sampling process, resulting in the generation of high-quality images with fine-grained textures and fewer artifacts. In addition, by utilizing the pre-trained unconditional diffusion model, the proposed model reconstructs the missing pixels in a zero-shot manner, which does not need any reference images for additional training. Extensive experimental results demonstrate that our proposed method is superior to the self-supervised tensor completion methods and representative diffusion model-based image restoration methods.

Remote sensing (RS) images are unavoidably contaminated by thick clouds, greatly limiting their subsequent application and exploration. Most existing conventional thick cloud removal methods are based on hand-crafted priors, which utilize the low-rank or smoothness property to regularize the latent RS images. However, these hand-crafted priors are failed to describe the rich structure that many RS images exhibit. Deep learning (DL) methods achieve their performance owing to extensive labeled training data while large-scale labeled data are expensive to acquire in the RS scene. In this paper, a thick cloud removal method named Spectral-Temporal Low-Rank regularization with Deep Prior (STLR-DP) is proposed to tackle these issues, solely using a single cloud-contaminated image without any extra external training data or pre-trained models, which utilizes an untrained neural network to capture the rich characteristic of RS images rather than hand-crafted priors. The spectral-temporal low-rank regularization is further incorporated into the model to avoid the over-fitting problem. Benefiting from the deep intrinsic image characteristic captured by the neural network and its self-supervised nature, our method can effectively simultaneously reconstruct the contour and details of contaminated regions, and can be adaptive to various RS images with strong generalization ability. Experimental results on simulated and real datasets demonstrate that the proposed STLR-DP method outperforms the representative thick cloud removal and tensor completion methods.

Most existing works on lightweight SAR ship detectors sacrifice a lot of detection accuracy to reduce model size. In this letter, we propose an ultra-lightweight detector based on distillation technology, which can reduce the parameter quantity of the model while minimizing the damage to the model’s detection accuracy. Due to the scattering interference and speckle noise in SAR images, directly applying the existing ultra-lightweight detectors cannot achieve satisfactory performance for ship detection. As a result, we design a global relationship distillation (GRD) algorithm for the ultra-lightweight SAR ship detector. This algorithm can preserve more global relationships from the teacher and mitigate the accuracy degradation caused by the noise and interference, especially in complex inshore scenarios. Besides, the features learned by this algorithm are robust, and the pruned model is more stable. The superiority of the GRD method over several state-of-the-art distillation methods has been evaluated on the HRSID dataset.

Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results.

Single image super-resolution technology is critical in remote sensing, effectively improving the resolution of target images, with super-resolution algorithms based on deep learning demonstrating superior performance. However, most neural networks present shortcomings, such as lack of interpretability and requiring a long training time, limiting them in some application scenarios. Moreover, due to multi-degradation factors, tasks put forward higher requirements for the adaptability of algorithms. Therefore, this work develops a structured deep unfolding network (SDUNet), which is adaptable and requires a lower training time by cascading multiple small network modules. Additionally, the unfolding strategy proposed deals with multiple degradations, fully exploiting prior knowledge. The suggested method is challenged against state-of-the-art neural network methods on one optical remote sensing image dataset and one natural image dataset. The experimental results demonstrate our method’s effectiveness in requiring less training time, involving fewer parameters, and achieving a higher reconstruction performance for optical remote sensing image super-resolution.

Single image super-resolution technology based on deep learning is widely applied in remote sensing. In recent years, the deep unfolding super-resolution strategy has been proposed, which combines the neural networks with traditional optimization-based algorithms, making the neural networks interpretable and achieving high performance. However, the typical deep unfolding algorithms usually treat different kinds of blurring kernels in the same way, so the algorithms cannot take advantage of the properties of blurring kernels, limiting the algorithm’s performance. To design a super-resolution network that can fully use the properties of Gaussian blurring kernels, a dual-resolution local attention unfolding network (DLANet) is proposed. Based on the Gaussian blurring functions, a low-resolution (LR) space branch is designed to supplement the high-resolution (HR) space branch. Specifically, for Gaussian blurring kernels, the closer the pixel is to the center, the greater the weight is. It means that the pixel points retained after downsampling will contain more information about the original corresponding pixel points, and it could be easier to estimate their original pixel values. So we design two branches. The HR branch completes the estimation of the whole image, and the LR branch only estimates the points retained after downsampling. To better complete the feature fusion of the two branches, we propose a row-column decoupling local attention module. This module can retain more information when fuse features and the row-column decoupling strategy can reduce computational complexity. Comprehensive experiments demonstrate the superiority of our method over the current state-of-the-art on remote sensing datasets.

Matrix and tensor completion aim to recover the incomplete two- and higher-dimensional observations using the low-rank property. Conventional techniques usually minimize the convex surrogate of rank (such as the nuclear norm), which, however, leads to the suboptimal solution for the low-rank recovery. In this paper, we propose a new definition of matrix/tensor logarithmic norm to induce a sparsity-driven surrogate for rank. More importantly, the factor matrix/tensor norm surrogate theorems are derived, which are capable of factoring the norm of large-scale matrix/tensor into those of small-scale matrices/tensors equivalently. Based upon surrogate theorems, we propose two new algorithms called Logarithmic norm Regularized Matrix Factorization (LRMF) and Logarithmic norm Regularized Tensor Factorization (LRTF). These two algorithms incorporate the logarithmic norm regularization with the matrix/tensor factorization and hence achieve more accurate low-rank approximation and high computational efficiency. The resulting optimization problems are solved using the framework of alternating minimization with the proof of convergence. Simulation results on both synthetic and real-world data demonstrate the superior performance of the proposed LRMF and LRTF algorithms over the state-of-the-art algorithms in terms of accuracy and efficiency.

With the development of synthetic aperture radar (SAR) system, automatic target recognition (ATR) has attracted wide attention in many decision-making tasks, in which an enhanced feature of SAR image is a powerful tool to improve the recognition accuracy. However, the presence of speckle noise and natural clutter inevitably contaminates SAR images and, thus, degrades image features. In this article, we explicitly address the speckle reduction problem for the circular SAR system, in which the motion of aircraft platform causes continuous angular variations so that different SAR images can be captured with the high interrelationship. By exploiting the underlying low-rank and continuous properties among different SAR images, a method called the ℓp-regularized low-rank and space-angle continuity extraction (ℓp-LSCE) is proposed to suppress the noise and enhance the target feature. Taking into account the interrelationship between SAR images, we arrange the images in a 3-D tensor to investigate the space-angle continuity of the targets. Furthermore, we develop a robust ℓp-regularized scheme to incorporate the low-rank property of targets. Then, the joint optimization problem is solved via the framework of augmented Lagrange multiplier (ALM) with efficient computation of each ALM subproblem. The experimental results of circular SAR data sets of the moving and stationary target acquisition and recognition (MSTAR) and the VideoSAR demonstrate that the proposed method can efficiently despeckle SAR images with well-preserved target features, which is conducive to the improvement of ATR performance.

Tensor robust principal component analysis via tensor nuclear norm (TNN) minimization has been recently proposed to recover the low-rank tensor corrupted with sparse noise/outliers. TNN is demonstrated to be a convex surrogate of rank. However, it tends to over-penalize large singular values and thus usually results in biased solutions. To handle this issue, we propose a new definition of tensor logarithmic norm (TLN) as the nonconvex surrogate of rank, which can decrease the penalization on larger singular values and increase that on smaller ones simultaneously to preserve the low-rank structure of a tensor. Then, the strategy of tensor factorization is combined into the minimization of TLN to improve computational performance. To handle impulsive scenarios, we propose a nonconvex 'p-ball projection scheme with 0 < p < 1 instead of the conventional convex scheme with p = 1, which enhances the robustness against outliers. By incorporating the TLN minimization and the 'p-ball projection, we finally propose two low-rank recovery algorithms, whose resulting optimization problems are efficiently solved by the alternating direction method of multipliers (ADMM) with convergence guarantees. The proposed algorithms are applied to the synthetic data recovery and image and video restorations in real-world. Experimental results demonstrate the superior performance of the proposed methods over several state-ofthe- art algorithms in terms of tensor recovery accuracy and computational efficiency.

Azimuth superresolution for real beam scanning radar aims to recover the high-resolution image from low-resolution echo. Among superresolution techniques, regularization-based methods are widely used, but most existing methods lead to the blurring of scattering targets and thus are difficult to distinguish between close targets. In this paper, we propose to employ the nonconvex ℓp-regularization with 0 <; p <; 1 to achieve the sparsity-driven superresolution, which further enhances the azimuth resolution. Furthermore, the resultant optimization problem is efficiently solved using an unified framework via incorporating different proximity operators. Simulation results validate the accuracy and efficiency of the proposed algorithm.

This paper proposes a method that can enhance the features of synthetic aperture radar images based on the exploitation of intrinsic target structure to improve the performance of automatic target recognition (ATR). We take advantage of the interrelationship between images and arrange them into a three-dimensional tensor. Then, by incorporating the joint low-rank and continuity constraints, the intrinsic target structure is extracted and enhanced with the reasonable suppression of speckle noise. Experiments on the moving and stationary target acquisition and recognition public database demonstrate the high quality of feature enhancement of the proposed algorithm, which efficiently improves the ATR performance.