Prediction-error expansion (PEE) is the most efficient approach in reversible data hiding (RDH). However, in PEE, to ensure the reversibility, significant distortion is introduced since many pixels are shifted without embedded data. Based on this consideration, a novel double-layered RDH framework called S+PEE is proposed in this paper. Unlike the conventional PEE, by S+PEE, shifted pixels can also be utilized for carrying secret data. The secret data is embedded in the first embedding layer with steganography and a specifically designed PEE-like mechanism. Then, to ensure the reversibility, the irreversible modifications introduced by the first embedding layer are recorded and embedded in the second embedding layer. Moreover, the corresponding capacity-distortion model is established to minimize the embedding impact, so that the marked image quality can be optimized. Experimental results demonstrate that the proposed method can provide high marked image quality, and it outperforms some state-of-the-art methods.

Under low-light conditions, perceptual fusion techniques, such as image fusion, can mitigate the inherent limitations of source images, thereby enhancing the safety performance of automated driving. However, these methods typically perform fusion and detection tasks separately, making it difficult to improve detection precision, as the fused images often lack the rich target information necessary for effective low-light traffic detection. In this paper, an end-to-end unified interactive network is proposed, which eliminates the problem of structural inconsistency between fusion and detection tasks by constructing interaction relationships. In this work, a dual-branch coupled feature extraction module is proposed. Different from other dual-branch methods, this module couples weak features from different modalities to enhance features. In addition, an interactive fusion module is proposed to achieve mutual enhancement between fusion and detection tasks while adaptively weighting the fusion of infrared and visible features. Extensive experimental results on traffic scene datasets, such as LLVIP and FMB, demonstrate that the proposed unified network not only achieves excellent fusion results but also significantly improves traffic object detection precision in low-light environments.

Electroencephalogram (EEG) is widely utilized in emotion recognition owing to its unique advantages. To achieve more optimal cross-subject emotion recognition, a cross subject emotion recognition method based on interconnection dynamic domain adaptation (IDDA) is proposed. In IDDA, dynamic graph convolution (DGC) is employed to dynamically learn the intrinsic relationships between different EEG channels and to extract domain invariant features. And dynamic domain adaptation (DDA) is employed to align the source domain and target domain, at the same time the emotional sub-domains is aligned, achieving more optimal cross subject emotion recognition. To select suitable subjects as the source domain, a multi-source selection algorithm is incorporated before dynamic adaptive computation reducing migration noise and achieving interconnection between DGC and DDA. IDDA enhances the emotion discrimination ability of domain invariant features, thereby improving the accuracy of cross-subject EEG emotion recognition. This method achieves classification results of 85.75% and 72.36% in cross subject experiments on SEED and SEED-IV.

Synthetic aperture radar (SAR) is a powerful imaging system with all-day and all-weather capabilities, making it suitable for a wide range of applications. However, SAR images often suffer from coherent speckle noise, which degrades image quality and hampers subsequent analysis and interpretation. Recently, methods based on the Fisher-Tippett (FT) distribution and nonlocal low-rank (NLR) techniques have shown great potential in SAR despeckling. Building upon these methods, this article proposes a novel SAR image despeckling method named SAR nuclear norm minus Frobenius norm (SAR-NNFN). This method effectively restores clean images using singular value shrinkage and allows for adaptive shrinkage without the need for additional weighting parameters. SAR-NNFN utilizes NNFN to achieve rank relaxation, resulting in a more robust low-rank solution for speckle reduction. The proposed model comprises two components: a data fidelity term that captures the statistical characteristics of SAR images using the FT distribution in the logarithmic domain, and an NNFN regularization term that enhances low-rank approximations. The optimization problem associated with SAR-NNFN is solved using the alternating direction method of multipliers (ADMM) algorithm. Extensive experiments conducted on both simulated and real SAR images demonstrate that SAR-NNFN can not only adequately suppress speckle noise but also preserve fine textures.

Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. However, most current ship detection networks rely too much on the anchor mechanism. These methods have low accuracy and poor generalization ability for multiscale ship detection. To solve the aforementioned problems, an anchor-free framework for multiscale ship detection in SAR images based on a balance attention network (BANet) is proposed. First, due to the diversity of scales and rotation angles of ships, deformable convolution is introduced to build a local attention module (LAM) to better obtain local information of ships and effectively enhance the robustness of the network. Second, a nonlocal attention module (NLAM) is designed to extract the nonlocal features of the SAR image, so as to balance the local features and nonlocal features acquired by the entire network. Finally, the feature pyramid network (FPN) is used to detect ships of different sizes at different scales. The detection results on three datasets demonstrate that the detection precision of our method is higher than that of all comparison methods, and this method achieves the most advanced performance.

Deep learning-based detection methods have achieved great success in ship target detection in synthetic aperture radar (SAR) images. However, due to the interference of imaging mechanism, speckle noise, and sea and land clutter, ship detection in SAR images still suffers from difficult interpretation. It is found that most ship detection algorithms focus on object-level detection while ignoring pixel-level information. In order to further improve the recognition effectiveness and positioning accuracy of ships in SAR images, we present a novel ship detection method based on a feature interaction network (FINet) in SAR images from the perspective of object-level and pixel-level. FINet consists of an object-level detection network and a pixel-level detection network. The information of the two branches is fused through the feature interaction module (FIM), and then the object-level information and pixel-level information are enhanced by the feature guidance module (FGM). Finally, FINet utilizes object-level and pixel-level detection heads for prediction and regression to obtain object-level classification accuracy, positioning bounding box coordinates, and pixel-level binary classification results. The experimental results demonstrate that the classification effectiveness and localization accuracy of FINet are better than those of the comparison algorithms, and FINet achieves the best performance.