Images captured under real-world nighttime haze conditions often suffer from severe degradations, including low visibility, color distortion, and reduced contrast, which not only impair visual perception but also degrade the performance of vision-based tasks. However, existing dehazing methods are mainly designed for daytime scenarios and struggle to cope with the complex illumination and scattering characteristics of nighttime hazy images. In this paper, we propose a novel Bayesian-based variational framework with fractional-order constraints for real-world nighttime image dehazing. First, a simplified physical model is constructed to characterize nighttime hazy images, accounting for haze, low-light conditions, Poisson noise, and glow degradations. An anisotropic pre-processing strategy is iteratively applied in the Lab color space to remove glow effects. Subsequently, illumination and reflectance estimation within our constructed physical model is formulated as a maximum a-posteriori (MAP) problem, which is then approximated as a unified variational optimization function. To impose prior constraints, two fractional-order terms are introduced as priors to regulate the illumination and reflectance, promoting piecewise smoothness in illumination and preserving sharp edges and fine textures in reflectance. The resulting variational model is efficiently solved using the alternating direction minimization method. Finally, the estimated illumination and reflectance are enhanced via spatial-domain gamma correction for brightness adjustment and frequency-domain processing for texture detail enhancement. Extensive experiments on real-world datasets demonstrate that the proposed framework outperforms state-of-the-art dehazing methods in both qualitative and quantitative evaluations. Besides, our algorithm generalizes effectively to both other degraded scenes and high-level vision tasks.

With the development of deep learning and the increase in the amount of data, general artificial intelligence models have become a popular research area nowadays. When facing a new application scenario, a pretraining general model can often show better performance than models trained with new data on its own. However, because of the specificity of the differences in hyperspectral image data bands, the current hyperspectral image classification (HSIC) field has not proposed a better general model training solution, and it is difficult to utilize the information of the existing hyperspectral datasets for model training in the face of a new scenario. In order to solve this problem, this article proposes a generalized hyperspectral classification model training method, which effectively completes the training of hyperspectral classification models across datasets by adaptive channel module and masked self-supervised pretraining method, and can pretrain and fine-tune hyperspectral classification models using multiple datasets. The adaptive channel module is able to solve the band difference problem of using hyperspectral datasets across datasets, and the masked self-supervised learning method solves the label difference and labeling difficulties of training models across datasets. Experimental results on multiple datasets show that the method proposed in this article can effectively use a large amount of data to complete the pretraining of hyperspectral classification models, and the fine-tuning results on downstream datasets have certain advantages relative to current advanced deep learning methods.

regularization model is hindered by their hypothesis of inherent sparsity, causing unreal estimations of surface-like targets. Inspired by the edge-preserving property of total variation (TV), we propose a new complex-valued TV (CTV)-driven interpretable neural network with nested topology, i.e., CTV-Net, for 3-D SAR imaging. In our scheme, based on the 2-D holography imaging operator, the CTV-driven optimization model is constructed to pursue precise estimations in weakly sparse scenarios. Subsequently, a nested algorithmic framework, i.e., complex-valued TV-driven fast iterative shrinkage thresholding (CTV-FIST), is derived from the theory of proximal gradient descent (PGD) and FIST algorithm, theoretically supporting the design of CTV-Net. In CTV-Net, the trainable weights are layer-varied and functionally relevant to the hyperparameters of CTV-FIST, which aims to constrain the algorithmic parameters to update in a well-conditioned tendency. All weights are learned by end-to-end training based on a two-term cost function, which bounds the measurement fidelity and TV norm simultaneously. Under the guidance of the SAR signal model, a reasonably sized training set is generated, by randomly selecting reference images from the MNIST set and consequently synthesizing complex-valued label signals. Finally, the methodology is validated, numerically and visually, by extensive SAR simulations and real-measured experiments, and the results demonstrate the viability and efficiency of the proposed CTV-Net in the cases of recovering 3-D SAR images from incomplete echoes.

Image resolution is the key point for the 3-D synthetic aperture radar (SAR) application, especially in small-scale scene observation. The traditional filter-based image enhancement algorithms used for 3-D SAR may suffer from quality degeneration in case of parameter mismatch. This paper proposes a robust and efficient convolutional neural network (CNN) based U-net framework for 3-D SAR image enhancement. The U-net extracts image features in down sampling and up sampling, which is realized by max pooling and deconvolution layers. We use the mean square error(MSE) as the loss function to estimate the difference between the predicted images and the label, while Adam optimizer updates parameters to achieve the global minimum MSE. Both simulation and measured data verify the effectiveness of the network. The results demonstrate that the U-net outperform some traditional filter-based algorithms.

SAR is usually subject to strong electromagnetic interference (EMI) during electronic reconnaissance missions, which will seriously weaken its ability of surveying and mapping. This paper presents a novel method for SAR image interference suppression based on the encoder-decoder network (named as ISEDnet). ISEDnet mainly consists of consecutive feature extraction net (FEN), the additional encoder-decoder network, and the image supervision mechanism. FEN is used to extract the features of interfered SAR images, and the Encoder-Decoder network (EDN) is used to suppress interference of SAR images. The image supervision mechanism is proposed to recover the target features. The network trained with simulation and real measurement data, the effectiveness of ISED-net are verified by both simulation and the Sentinel-1 satellite SAR images. Compared to the traditional notch filtering method, ISEDnet can successfully suppress different types of SAR interference and improve interference suppression performance.

The compressed sensing (CS) method is widely utilized in the field of radar sparse imaging. However, it always encounters enormous iterations and low generalizability. To solve these problems, in this paper, we propose a novel learning-based sparse imaging network architecture, i.e., Split Unfolding Sparsity-Driven Network (SSD-Net), for 3D synthetic aperture radar (SAR) imaging. By combining the model-based SAR imaging method and data-driven deep learning method, SSD-Net has favorable explainability and generalization abil-ity to produce 3D SAR images. The deep hierarchical ar-chitecture of SSD-Net is obtained by combiningthe radar nonlinear operator and the split Bregman method. The exper-iments demonstrate that the proposed SSD-Net outperforms other state-of-the-art methods in the field of SAR imaging.

The multimodal representation of signals can give a lot of information in the field of communication modulation signal recognition. Existing approaches can use some of the modes for classification tasks, but they don't take use of the multimodal characteristics of signals well. The approach of signal modulation recognition suggested in this research is based on reinforcement learning and multimodal features. The feature set is obtained via wavelet transform using the 1D features of the signal and the constellation map features, and then the feature set is filtered using reinforcement learning to get a small subset of features to obtain the training feature set. With a small number of extra channels, the accuracy of recognition can be enhanced when utilizing the filtered feature set to categorize the modulation class of the signal. Experiments on the RML2016 dataset validate the effectiveness of our proposed method and achieve good classification performance, which is a pioneering idea for the signal modulation classification problem.

Compressed sensing (CS) shows significant potential in the field of active millimeter-wave (mmW) synthetic aperture radar (SAR) imaging due to the merits of reducing system complexity and achieving high-speed sensing. However, most CS-driven imaging methods suffer from the excessive computational burden, since the calculative steps always rely on vectorization and consequently lead to extremely large-scale matrix operations. To address this issue, we propose an efficient alternating direction method of multipliers (ADMMs) framework for mmW 3-D SAR imaging. In our scheme, we utilize the single-frequency holographic (SFH) technique and construct SFH-based forward/inverse sensing operators rather than converting the imaging process into a special case of “linear inverse problems,” by which the large-scale matrix inversions are avoided and consequently the computational complexity is reduced. Based on the SFH functional measurement model, the SFH-ADMM is derived to reconstruct the 3-D image from sparsely sampled measurement echo while suppressing noisy clutters and ambiguities. Besides, the SFH-ADMM iteration steps undergird a neural network design, yielding a tailored SFH-ADMM-Net with trainable parameters and layer-fixed structures, which further shorten the execution time and improve reconstruction performance. The network is trained by simulated data, which are generated according to the radar signal model. Extensive experiments, including simulations and laboratory tests, demonstrate the superiority of the proposed algorithms in terms of both reconstruction accuracy and computational speed.

In the research topic of three-dimensional (3D) SAR imaging, the sparsity-enforcing techniques offer promise in shortening sensing time and improving reconstruction accuracy. However, many of them only explore the sparse prior of 3D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors. Wherein, the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3D SAR images with a fast-iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyper-parameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3D SAR images from incomplete echo data.

Inevitable inaccuracies of 3-D synthetic aperture radar (3-D SAR) imaging geometry may cause undesired blurs in reconstructed images. Recent advances show impressive results in integrating error estimation into sparse imaging. However, the concept is still challenging in 3-D SAR due to the cumbersome high-dimensional processing. To address this problem, we propose a model-driven 3-D SAR autofocusing network with learned sparsity (AFLS-Net) by applying the recent emerging deep unfolding technique. In our scheme, we first construct a kernel-based observation model with consideration of motion-induced phase errors, which avoids the memory-consuming matrix calculations in the conventional matrix–vector form. Then, a joint sparse imaging and autofocusing algorithm is derived based on the framework of block coordinate descent. In addition, by mapping the computational steps, the AFLS-Net is designed to further improve the autofocusing accuracy and efficiency in which a shallow two-path convolutional neural network (CNN) is embedded to explore the implicit sparse prior, by which the reconstruction accuracy can be improved. Meanwhile, the batchwise autofocusing module is designed to obtain a robust estimation by jointly optimizing subcost functions associated with a batch of independent measurements. Finally, the methodology is validated in both simulations and laboratory 3-D SAR experiments. The experimental results suggest that the proposed method obtains better autofocusing quality compared to other comparison baselines in reconstructing 3-D SAR images from incomplete and error-polluted echoes.

The application of the compressed sensing (CS) method in the radar field enables the radar imaging system to satisfy both low data cost and high reconstruction quality, however, it is accompanied by enormous iterative operations and difficult adjustments of parameters. In this paper, we propose a learning-based split unfolding framework, dubbed as split iterative sparse reconstruction network (SISR-Net), for near-field 3-D millimeter-wave (mmW) radar sparse imaging. Firstly, a sparse reconstruction algorithm, i.e., SISRA, is proposed to theoretically guide the structure of the imaging framework. Subsequently, by combining the model-based CS method and data-driven deep learning method, SISR-Net is constructed by SISRA to produce 3-D mmW radar images efficiently with excellent explainability and generalization ability. Joint the radar-imaging kernel, echo-generation kernel, and the split Bregman method, the efficiency and stability of SISR-Net are guaranteed, all parameters are layer-varied and learned steadily by end-to-end training to improve the convergence and robustness of the imaging network. Simulated data and the echo from a high-resolution mmW radar dataset 3DRIED, are used to train and test the SISR-Net based on the Adam optimizer. For both simulation and extensive 3-D mmW radar measured experiments, the proposed SISR-Net outperforms other state-of-the-art imaging methods in terms of imaging accuracy and generalization ability.

Ship instance segmentation of high-resolution SAR images is a valuable and challenging task due to the complex scattering and noise properties. In this article, we pioneered the construction of the low-level feature to discriminate the ships and complemented the super-resolution denoising techniques in the network modules, termed low-level feature guided network (LFG-Net), for precise ship instance segmentation in SAR images. LFG-Net consists of the low-level feature concerned pyramid (LFCP), the high-resolution interaction module (HR-FIM), and the compression recovery segmentation branch (CRSB). LFCP extends vanilla FPN with the P1layer and complements super-resolution techniques to capture the regional and texture information at the image level for small object segmentation. HR-FIM interacts the bounding box region of interest (RoI) feature and mask RoI feature at the instance level with high-resolution techniques to enhance the mask RoI feature. CRSB aims at recovering the high-resolution mask predictions to improve the ship segmentation performance. Comprehensive experiments on HRSID, PSeg-SSDD, and AirSARShip indicate that LFG-Net* achieves 11.7%, 6.3%, and 12.7% AP increments compared with the Mask R-CNN baseline, respectively. Besides, it receives 9.5%, 4.9%, and 7.3% AP increments compared with state-of-the-art method, respectively, which bridges the gap of instance segmentation precision in SAR images. In terms of the visualized instance segmentation results, LFG-Net* is capable of segmenting the complex scenes, e.g, the adjacent distributed ships and ships with strong reflection noise interference, in SAR images. Code is available at: https://github.com/Evarray/LFG-Net.

The sparse imaging method based on compressed sensing (CS) is widely used in the field of millimeter-wave (MMW) synthetic aperture radar (SAR) imaging. However, 3D sparse imaging is limited by the difficult parameter tuning, the huge computational load, and the low processing efficiency. In addition, due to the motion errors and model mismatch, it is difficult to obtain well-focused results without error correction techniques. To address these issues, we propose a deep learning framework that integrates 3D sparse imaging and autofocusing, named 3D Sparse Autofocusing Network (SAF-3DNet) for MMW SAR data processing. The network is constructed based on an auto-encoder, which can optimize parameters without effective ground truth. The backbone structure of the encoder is expanded by approximate message-passing (AMP), and the operators in the frequency domain are used to replace the traditional matrix-vector CS model, which avoids large-scale matrix multiplication and other operations, and greatly improves the operation efficiency. In addition, the 2D phase error estimation in the cross-range plane is embedded into the sparse imaging models, enabling simultaneous 3D imaging and autofocusing. The decoder is designed as a mapping from the autofocusing results to the echo data. Experimental results based on both simulated and measured data demonstrate the proposed SAF-3DNet can achieve well-focused 3D reconstruction within an ephemeral time, which expresses the potential of 3D MMW SAR real-time and high-quality imaging.

The emerging field of combining compressed sensing (CS) and three-dimensional synthetic aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate and improve image quality. However, the conventional CS-driven algorithms are always limited by huge computational costs and non-trivial tuning of parameters. In this article, to address this problem, we propose a two-path iterative framework dubbed TPSSI-Net for 3D SAR sparse imaging. By mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the AMP optimization. Differently, the Onsager terms in TPSSI-Net are modified to be differentiable and scaled by learnable coefficients. Rather than manually choosing a sparsifying basis, a two-path convolutional neural network (CNN) is developed and embedded in TPSSI-Net for nonlinear sparse representation in the complex-valued domain. All parameters are layer-varied and optimized by end-to-end training based on a channel-wise loss function, bounding both symmetry constraint and measurement fidelity. Finally, extensive SAR imaging experiments, including simulations and real-measured tests, demonstrate the effectiveness and high efficiency of the proposed TPSSI-Net.