For remote sensing image fusion, infrared and visible images have very different brightness due to their disparate imaging mechanisms, the result of which is that nontarget regions in the infrared image often affect the fusion of details in the visible image. This letter proposes a novel infrared and visible image fusion method basing hybrid representation learning by combining dictionary-learning-based joint sparse representation (JSR) and nonnegative sparse representation (NNSR). In the proposed method, different fusion strategies are adopted, respectively, for the mean image, which represents the primary energy information, and for the deaveraged image, which contains important detail features. Since the deaveraged image contains a large amount of high-frequency details information of the source image, JSR is utilized to sparsely and accurately extract the common and innovation features of the deaveraged image, thus, accurately merging high-frequency details in the deaveraged image. Then, the mean image represents low-frequency and overview features of the source image, according to NNSR, mean image is classified well-directed to different feature regions and then fused, respectively. Such proposed method, on the one hand, can eliminate the impact on fusion result suffering from very different brightness causing by different imaging mechanism between infrared and visible image; on the other hand, it can improve the readability and accuracy of the result fusion image. Experimental result shows that, compared with the classical and state-of-the-art fusion methods, the proposed method not only can accurately integrate the infrared target but also has rich background details of the visible image, and the fusion effect is superior.

The image fusion method based on sparse representation in the single‐scale image domain has produced better fusion results than the classic methods based on multi‐scale analysis nowadays. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse‐representation‐based image fusion methods, and it requires a lot of time. A novel dictionary is constructed with non‐subsampled contourlet transform and sparse representation by using the proposed simultaneous strategy. Then the novel dictionary could combine the sparsity attribute of the learning dictionary with a multi‐scale feature of non‐subsampled contourlet transform. Moreover, the simultaneous strategy is combined with this novel dictionary so that sparse coefficients can be represented with the same dictionary atoms and thus they can be compared in a reasonable and accurate way. Finally, the image fusion method along with this novel dictionary is proposed and named non‐subsampled contourlet transform (NSCT)–simultaneous sparse representation (SSR). Experimental results show that the proposed fusion method NSCT–SSR, with its more excellent fusion effect and better anti‐noise capability, outperforms the existing fusion methods, which are based on both multi‐scale domain and sparse representation in the single‐scale image domain.

Millimeter-wave integrated access and backhaul network (mmWave IABN) is a cost-effective paradigm to accommodate the ever-increasing demands of IoT devices, but its sensitivity to the obstructions is an obstacle in the practical commercial applications. Reconfigurable intelligent surface (RIS) is an innovation enabler to proactively manipulate the ambient environment in a programmable manner for the overall performance enhancement. In this paper, the resource allocation of the RIS-assisted mmWave IABN is conceived and designed to maximize the end-to-end throughput. Firstly, the end-to-end throughput maximization oriented resource allocation problem including transmission power controlling, phase-shift manipulation, and bandwidth allocation is formulated as a non-linear and non-convex programming problem, which is intractable to search an optimal solution in polynomial time. Secondly, the formulated original problem is equivalently decomposed into two subproblems to obtain a sub-optimal solution, i.e., the joint optimization subproblem of transmission power controlling at the users and phase-shift manipulation at the RIS, and the bandwidth allocation subproblem between the access part and the backhaul part. Thirdly, a decomposition iteration based resource allocation mechanism (DIRAM) is proposed, and the DIRAM is composed of two phases, which is the alternative iteration based optimization scheme of power controlling and phase-shift manipulation (AIOS-PCPM) to obtain the optimal solution of the first subproblem, and the piecewise statistical based bandwidth allocation (PSBA) scheme to obtain the optimal solution of the second subproblem. Finally, the properties of the proposed DIRAM are evaluated through abundant of simulation comparisons with other baselines, where the superiorities of the proposed DIRAM are verified in terms of spectral efficiency and end-to-end throughput.

In this paper, a novel loss function is proposed to measure the correlation among different learning tasks and select useful feature components for each classification task. Firstly, the knowledge map we proposed is used for organizing the affiliation relationship between objects in natural world. Secondly, a novel loss function–orthogonality loss is proposed to make the deep features more discriminative by removing useless feature components. Furthermore, in order to prevent the extracted feature maps from being too divergent and causing over-fitting which will reduce network performance, this paper also added the orthogonal distribution regularization term to constrain the distribution of network parameters. Finally, the proposed orthogonality loss is applied in a multi-task network structure to learn more discriminative deep feature, and also to evaluate the validity of the proposed loss function.The results show that compared with the traditional deep convolutional neural network and a multi-task network without orthogonality loss, the multi -task based orthogonality loss is significantly better than the other two types of networks on image classification.

The widespread application of convolutional neural networks (CNNs) has led to significant advancements in object detection. However, challenges remain in achieving efficient and precise extraction of critical features when applying typical CNN-based methods to remote sensing detection tasks: (1) The convolutional kernels sliding horizontally in the backbone are misaligned with the features of arbitrarily oriented objects. Additionally, the detector shares the features extracted from the backbone, but the classification task requires orientation-invariant features while the regression task requires orientation-sensitive features. The inconsistency in feature requirements makes it difficult for the detector to extract the critical features required for each task. (2) The use of deeper convolutional structures can improve the detection accuracy, but it also results in substantial convolutional computations and feature redundancy, leading to inefficient feature extraction. To address this issue, we propose a Task-Sensitive Efficient Feature Extraction Network (TFE-Net). Specifically, we propose a special mixed fast convolution module for constructing an efficient network architecture that employs cheap transform operations to replace some of the convolution operations, generating more features with fewer parameters and computation resources. Next, we introduce the task-sensitive detection module, which first aligns the convolutional features with the targets using adaptive dynamic convolution based on the orientation of the targets. The task-sensitive feature decoupling mechanism is further designed to extract orientation-sensitive features and orientation-invariant features from the aligned features and feed them into the regression and classification branches, respectively, which provide the critical features needed for different tasks, thus improving the detection performance comprehensively. In addition, in order to make the training process more stable, we propose a balanced loss function to balance the gradients generated by different samples. Extensive experiments demonstrate that our proposed TFE-Net can achieve superior performance and obtain an effective balance between detection speed and accuracy on DOTA, UCAS-AOD, and HRSC2016.