Infrared and visible image fusion (IVIF) aims to extract fine details from visible images and complementary information from infrared images. Most existing methods directly extract relevant and complementary features from each modality using neural networks, often overlooking the guidance process and the distinct frequency-domain characteristics of these features. To address this, we propose HRFusion-a novel frequency-domain framework that extracts complementary features from hybrid features using prior-constrained relevant features, effectively enhancing complementary information and reducing redundancy. In HRFusion, hybrid and relevant features are robustly extracted to guide the subsequent fusion stage. By leveraging frequency differences between complementary and relevant features, we introduce the Enhanced Complementary Frequency Network (ECFNet), which uses optimized Variational Mode Decomposition (VMD) to effectively separate and process these signals for fusion. The overall architecture is built with the proposed DTBlock, which captures both global and local features. Extensive experiments show that our method achieves state-of-the-art performance on the TNO, MSRS, M3FD, and Harvard Brain datasets, outperforming recent approaches. Code is available at https://github.com/liuuuuu777/HRFusion.

Monocular depth estimation stands as a fundamental pursuit in computer vision. Recently, some methods have attempted to introduce the text-to-image diffusion model into the domain of monocular depth estimation and achieved impressive results. However, these methods typically employ pre-defined templates as text prompts to guide the learning of denoising networks, resulting in limited flexibility and scalability. In this paper, we propose OGDepth, a diffusion-based monocular depth estimation network with object prompts generated by taking advantage of the object detection information from the scene. Specifically, we design an Object Prompt Module (OPM) to encode the object detection information into prompts that are more closely aligned with the image content, offering richer contextual information while circumventing the monotony and redundancy inherent in template-generated prompts. Moreover, we employ bounding box information for each object to filter and localize objects, enabling the model to grasp relative positional information within the scene. This facilitates the creation of a more precise depth map. Additionally, we design a Global-Local Interaction Decoder (GLID) to facilitate the mutual exchange of features at different scales, enabling efficient feature fusion. Our approach underwent rigorous experiments across multiple datasets, with results showcasing its state-of-the-art performance. Notably, on the KITTI dataset, our model achieves an RMSE of 1.967 and a REL of 0.047, and both metrics are the best among all compared methods. On the NYU Depth V2 dataset, our method achieves an RMSE score of 0.221, representing a notable 12.9% enhancement compared to the baseline method (VPD).

Stereo matching plays an important role in advancing substantial applications such as 3D reconstruction, autonomous driving, depth sensing, and has recently made tremendous progress. It still poses a significant challenge, however, in the extraction of disparities from a pair of rectified images to achieve accurate stereo matching. In this paper, we propose an attention-fused cost volume construction and progressive cost aggregation network, namely AP-Net, to elevate the accuracy of stereo matching. Specifically, we introduce attention fusion to learn feature similarity between corresponding points on the left and right images while capturing long-dependent contextual information to construct attention-fused cost volumes. Moreover, we propose progressive cost aggregation, which consists of two stages to fully exploit information from different cost volumes. Furthermore, to make more accurate adjustments to the initial disparity, we implement an information entropy-guided disparity refinement module, which leverages information entropy as an explicit confidence measure of the initial disparity estimation to guide the refinement module in calculating the disparity residual more accurately and improving the overall accuracy of disparity estimation. Our implemented disparity refinement module can also be seamlessly embedded into a variety of stereo matching networks, significantly improving the model’s performance with only a small increase in computation and parameters. Experimental results across various stereo datasets confirm that our proposed AP-Net consistently delivers competitive performance. The code is available at https://github.com/zhuys-bupt/AP-Net .

This paper presents an practical RSSI based TOA ranging error model (RITEM) for localization algorithm, which can be used to estimate ranging error interval in real time. In RITEM, ranging error is classified into four classes by the RSSI value in TOA ranging process and ranging error of each class always within a certain interval. RITEM is verified by field tests in two typical indoor environments. Then, RITEM is applied into Ranging Error Classification (REC) based TOA localization algorithm to introduce its application methodology. Experiment result indicates that REC algorithm has significantly improved performance in typical indoor environment, comparing with LS, CN-TOAG and Nano localization algorithms.