Infrared image colorization has witnessed notable advances in recent years; however, existing methods still suffer from color distortions, edge blurring, and artifacts, particularly in complex scenes. To mitigate these issues, we propose a Semantic Segmentation-Guided Region-Attention (SSRA) framework, which enhances colorization fidelity with a stronger emphasis on semantically salient regions. Specifically, we employ the Segment Anything Model (SAM2) to produce high-quality semantic masks, enabling regional self-attention to operate within consistent object boundaries. This design effectively suppresses background interference and facilitates more precise color reconstruction in foreground regions. Furthermore, a focal region loss is introduced to adaptively weight reconstruction errors based on regional importance, thereby directing the model’s representational capacity towards critical areas such as vehicles, pedestrians, and road infrastructure. Extensive experiments on two drone-acquired datasets validate the efficacy of our approach, demonstrating superior performance in both quantitative metrics and qualitative assessments, with notable improvements in structural detail preservation and semantic-aware color consistency.

In environments where vision-based depth estimation systems, such as those utilizing infrared or imaging technologies, encounter limitations—particularly in low-light conditions—alternative approaches become essential. Echo depth estimation emerges as a compelling solution by leveraging the time delay of echoes to map the geometric structure of the surrounding environment. This method offers distinct advantages in specific scenarios, providing reliable data for accurate scene understanding and 3D reconstruction. Traditional echo depth estimation techniques primarily depend on spatial information captured by the encoder and depth predictions made by the decoder. However, these methods often fail to fully exploit the rich depth features present at different simultaneous frequencies. To address this challenge, we propose an echo depth estimation method via Attention-based Hierarchical Multi-scale Feature Fusion Network (AHMF-Net). This network is designed to extract spatial depth information from echo spectrograms across multiple scales and hierarchical levels, while fusing the most relevant information using an attention mechanism. AHMF-Net introduces two key modules in hierarchical levels: the Intra-layer Multi-scale Attention Feature Fusion (IMAF) module, which functions as the encoder to capture multi-scale features across varying granularities, and the Inter-layer Multi-Scale Detail Feature Fusion (IMDF) module, which integrates features from all encoding layers into the decoder to enable effective inter-layer multi-scale fusion. Additionally, the encoder incorporates an attention mechanism that enhances depth-related features by capturing channel dependencies at multiple scales. We evaluated AHMF-Net on the Replica, Matterport3D, and BatVision datasets, where it consistently outperformed state-of-the-art models in echo-based depth estimation, demonstrating superior accuracy and robustness. The source code is publicly available at https://github.com/wjzhang-ai/AHMF-Net .

Due to its portability, low cost, and unnoticed detection mode, passive non-line-of-sight (NLOS) imaging has garnered widespread attention in recent years. This technology reconstructs hidden objects beyond the direct line of sight by analyzing the diffuse reflection on a relay surface. However, issues such as scene complexity and interference from ambient light lead to suboptimal reconstruction results. Long-wave infrared (LWIR) imaging is more significantly affected by ambient temperature but less by illumination, resulting superior resistance to environmental light interference. Therefore, we conduct NLOS imaging experiments using an LWIR camera and propose a novel NLOS image reconstruction network called Nestformer. The feature extraction component of this network incorporates a Transformer-CNN dual-branch mechanism. This dual-branch mechanism includes a Base Transformer Module for capturing global features and a Spatial Channel Attention Module that focuses on extracting local information. Additionally, to enhance the robustness of our model, a combination of multiple loss functions is employed to optimize its feedback capability. Experimental results on our self-collected NLOS-LR dataset indicate that Nestformer outperforms current passive NLOS imaging methods in terms of reconstruction performance.

The combination of visible and infrared images is intended to facilitate complex vision tasks by combining target information and rich texture. By focusing solely on visual perception enhancement, current fusion algorithms do not take into account performance on high-level vision tasks. As a solution to these problems, this research develops a high-level vision task-driven image fusion network (TaDFusion) that combines image fusion and target identification tasks. Through cascading of the image fusion and target detection modules we can significantly improve the performance of advanced vision tasks by using detection loss to guide the information back to the image fusion module. Our algorithm provides better texture preservation and pixel intensity distribution than existing methods based on extensive comparisons and generalization experiments. Besides our framework demonstrates the greatest advantages in facilitating advanced vision tasks by not only generating visually appealing fused images but also detecting higher mAPs than state-of-the-art methods, according to a comparison of the performance of various fusion algorithms in target detection tasks.

Synthetic aperture radar (SAR) has emerged as a new tool for tropical cyclone (TC) monitoring by providing information on the location of TC centers. However, SAR does not usually cover the entire TC domain due to its limited swath width. In this paper, we develop a procedure to identify the location of the center of a TC when an SAR image only covers the rain band portion of the TC but not the eye. The algorithm is based on both an image processing procedure and the available knowledge of the inherent rain-band structure of a TC. The three-step algorithm includes: 1) applying a Canny edge detector to find the curves associated with rain bands; 2) defining two filter criteria to select the spiral curves that resemble the estimation based on a TC rain-band model; 3) searching for the optimal matching solution using the particle swarm optimization algorithm. Numerical experiments with images without TC eye information show that the proposed method can effectively locate the centers of TCs. We compare the experimental results with the best track data to indicate the accuracy. Then, we compare the inflow angle model and the logarithmic spiral model and find that the inflow angle model is more accurate for TC center identification.

Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution, large area coverage, day/night imaging capability, and penetrating cloud capability, has been used as an important tool for tropical cyclone monitoring. The accuracy of locating tropical cyclone centers has a large impact on the accuracy of tropical cyclone track prediction. Usually, the center of a tropical cyclone can be accurately located if the tropical cyclone eye is fully covered by a SAR image. In some cases, due to the limited coverage of the SAR, only a part of a tropical cyclone can be imaged without the eye. From a SAR image processing point of view, these facts make the automatic center location of tropical cyclones a challenging work. This paper addresses the problem by proposing a semiautomatic center location method based on salient region detection and pattern matching. A salient region detection algorithm is proposed, in which the salient region map contains mainly the rain bands of a tropical cyclone in a SAR image. The pattern matching problem is transformed into an optimization problem solved by using the particle swarm optimization algorithm to search the best estimated center of a tropical cyclone. To estimate the accuracy of the located center, we compare the results with the NOAA National Hurricane Center's best track data. Experiments demonstrate that the proposed method achieves good accuracy for locating the centers of tropical cyclones from SAR images that do not contain a distinguishable eye signature.

Automatic hurricane information extraction in synthetic aperture radar (SAR) images has been a research topic in development. In this study, using saliency-based visual attention model, we developed an image processing procedure to extract hurricane eyes from SAR images. Experiment results show that hurricane eyes can be well extracted even when it is not visually obvious in images.