Image fusion has been received much attentions in recent years. However, solving both noise-free image fusion and noise-perturbed image fusion problems remains a big challenge. To solve the weak performance and low computational efficiency for current image fusion methods when dealing with the case of noisy source images, an image fusion method based on three-layer decomposition and sparse representation is proposed in this paper. In view of the high-pass characteristics of noise, the source image is first decomposed into the high-frequency and low-frequency components, and the sparse reconstruct error parameter is adaptively designed according with the noise level, so as to realize the fusion and denoising for high-frequency components simultaneously. To make full use of the details and energy in the low-frequency component, the structure–texture​ decomposition model is carried out and two fusion rules are carefully designed to fuse them. The fused image can be reconstructed by the perfused high-frequency, low-frequency structure and low-frequency texture layers. Experimental results demonstrate that the proposed method can effectively address the clean and noisy image fusion problems, and yield better performance than some state-of-the-art methods in terms of subjective visual and quantitative evaluations.

Joint image fusion and denoising via three-layer decomposition and sparse representation

Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.

https://www.mdpi.com/2072-4292/13/13/2432/pdf

Atmospheric Light Estimation Based Remote Sensing Image Dehazing

Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.

Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

A non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) framework is investigated. A dual-functional base station serves multiple communication users employing NOMA, while the superimposed NOMA communication signal is simultaneously exploited for target sensing. A beamforming design problem is formulated to maximize the weighted sum of the communication throughput and the effective sensing power. To solve this problem, an efficient double-layer penalty-based algorithm is proposed by invoking successive convex approximation. Numerical results show that the proposed NOMA-ISAC outperforms the conventional ISAC in the underloaded regime experiencing highly correlated channels and in the overloaded regime. 

NOMA Empowered Integrated Sensing and Communication

Conventional hybrid precoding and combining based transceivers require a large number of high-resolution radio frequency (RF) phase shifters (PSs), which impose prohibitive hardware costs and power consumption. To address the above issue, both partially connected RF PSs and low-resolution PSs have been proposed. However, the performance limits of these low-cost designs have not been investigated theoretically. Furthermore, there is room for improvement in their spectral efficiency. To fill this knowledge gap, we derive the mean square error performance discrepancy between an optimal precoder/combiner and the hybrid analog-digital precoder/combiner under the constraint of 1-bit PSs relying on lattice theory. Then, by observing that this performance gap can be reduced by deactivating parts of the PSs whilst improving both the spectral and energy efficiency, we develop an adaptive RF PS connection network. To resolve the associated hybrid precoding and combining problems, we appropriately adapt Babai’s algorithm from the lattice decoding literature. Our simulation results demonstrate the superiority of the proposed scheme both in terms of its spectral and energy efficiency.

/pdf/lattice-based-mmwave-hybrid-beamforming-5eiyqvmi28.pdf

Lattice-Based mmWave Hybrid Beamforming

In this letter, a gridless one-bit direction-of-arrival (DOA) estimation approach with one-snapshot is proposed to be robust against the off-grid errors and sign inconsistency because of the one-bit measurements corrupted by additive noise. Different with the existing off-grid DOA estimators, an atomic norm minimization is considered to mitigate the grid mismatch with atoms instead of pre-divided discretized dictionary. Then, the sign inconsistency of one-bit measurements is solved by introducing a linear loss function. The resulting optimization problem is convex and can be equivalent to a semidefinite programming (SDP) problem, which, however, is computational demanding. Therefore, alternating direction multiplier method (ADMM) is employed to speed up the implementation. To avoid the spectrum searching, an effective dual polynomial method is developed with closed-form solution for DOA estimation. Meanwhile, the proposed method does not require $a~prior$ information of the number of targets. Simulation results demonstrate the superiority and effectiveness of the proposed method.

Gridless One-Bit Direction-of-Arrival Estimation Via Atomic Norm Denoising

Large-scale antennas systems have been attracted significant attention to provide considerable array gains for millimeter wave (mmWave) communications systems. However, huge hardware complexity and power consumptions make the conventional precoding architectures impractical to realize. Recently, hybrid precoding structures for mmWave communications systems are widely investigated to save the cost and achieve satisfactory performance. Unfortunately, most existing precoder designs lie in the assumption of infinite or high-resolution phase shifters (PSs), which are also difficult to realize in real-world. Motivated by this fact, a low complexity hybrid precoder and combiner design for multi-user systems with two-bit resolution PSs is proposed in this paper, which provides an appropriate trade-off between energy efficiency and system sum-rate. Specifically, the candidate sets for analog beamforming are constructed by introducing the auxiliary variables, in which the number of elements is the same as that of antennas. Accordingly, the computational complexity of searching optimal beamformer is significantly reduced over proposed codebooks. Then, to avoid high complexity singular value decomposition (SVD) of channel matrix, we propose a rank-1 approximate decomposition algorithm based-on principal component analysis technique. At last, the user scheduling algorithm is presented to further improve the performance in multiuser-scenario. Simulation results verified the effectiveness of the proposed algorithms, the performance of which is close to the optimal unquantized precoder.

Low-Complexity Hybrid Precoding for Multi-User MmWave Systems With Low-Resolution Phase Shifters

Image deterioration problem occurs in radar imaging for ship target, which results from the complex time-varying motions of ship, the noise in channels, and the clutter on sea surface. It is hard to be solved effectively due to coherent accumulation sampling time and high-dimensional parametric model. Hence, an accurate imaging and motion estimation method based on multiple-input–multiple-out (MIMO) radar is presented. First, the multidimensional signal model is built to characterize target features accurately. To reduce the interference from sea clutters, a preprocessing strategy is exerted based on the space–time adaptive processing (STAP) theory, clutter signals can be suppressed effectually with constructing a Doppler spectrum model. Then, for accurate imaging and motion estimation, a combined trace norm minimization problem is deduced based on the relaxation of tensor rank, where the noise in sea environments is also considered. Meanwhile, generalized tensor total variation constraint is developed to ensure stable estimation and smooth imaging results when separating the noise term. Accordingly, an effective decomposition criterion is formulated based on alternating direction multiplier method (ADMM) strategy, and motion parameters can be precisely calculated based on the least square (LS) method. Finally, theoretical analysis and simulation results present the accurate performance of the proposed method.

MIMO Radar Accurate Imaging and Motion Estimation for 3-D Maneuvering Ship Target

Joint radar and communication (JRC) system is expected to significantly improve the utilization of spectrum resources, system performance, and reduce the equipment cost and size. In this letter, a JRC system containing a collocated multi-input multi-output (MIMO) radar and a multi-user communication base station is considered, which can serve several downlink users and detect multiple targets simultaneously. The radar signal covariance matrix and the communication physical precoder are jointly designed by matching the desired beampattern for radar detection while maximizing the downlink communication signal to interference and noise ratio (SINR). By relaxing the original problems, the iterative process with closed-form expressions is proposed to obtain the sub-optimal solutions of the system. Compared with solving the optimization problems directly, the proposed algorithm can significantly reduce the computational complexity with a little loss of system performance. Finally, simulation results show the effectiveness of the proposed algorithm.

Low-Complexity Beamformer Design for Joint Radar and Communications Systems

Lens antenna array (LAA), as a promising architecture in millimeter-wave (mmWave) massive MIMO systems, which can considerably reduce the number of required radio-frequency (RF) chains. In this article, we propose a high-resolution channel or angle-of-arrival (AoA) estimation scheme while taking into account the power leakage problem. Specifically, the criterion function is constructed based on DFT beam difference (DBD) to infer the real position of arrived signal. Subsequently, the novel antenna selection and path number estimation algorithms are proposed without prior knowledge of channel. A few groups of antennas are selected to connect to the RF chains, where each group contains only a small number of antennas. Thus, the beamspace multiple signal classification (MUSIC) algorithm can be implemented in low dimensional space to estimate the AoA(s) for each group. Then the channel estimation can be obtained by least square method after the reconstructions of steering vectors using the AoA estimations. Furthermore, the corresponding spatial smoothing algorithm is leveraged to tackle rank deficient problem at the scenario of constant path gains. Simulation results verify the effectiveness of the proposed scheme.

Fuwang Dong

Papers

Gridless One-Bit Direction-of-Arrival Estimation Via Atomic Norm Denoising

Low-Complexity Hybrid Precoding for Multi-User MmWave Systems With Low-Resolution Phase Shifters

MIMO Radar Accurate Imaging and Motion Estimation for 3-D Maneuvering Ship Target

Low-Complexity Beamformer Design for Joint Radar and Communications Systems

High-Resolution Angle-of-Arrival and Channel Estimation for mmWave Massive MIMO Systems With Lens Antenna Array