Infrared and visible image fusion can effectively integrate the advantages of two source images, preserving significant target information and rich texture details. However, most existing fusion methods are only designed for well-illuminated scenes and tend to lose details when encountering low-light scenes because of the poor brightness of visible images. Some methods incorporate a light adjustment module, but they typically focus only on enhancing intensity information and neglect the enhancement of color feature, resulting in unsatisfactory visual effects in the fused images. To address this issue, this paper proposes a novel method called EV-fusion, which explores the potential color and detail features in visible images and improve the visual perception of fused images. Specifically, an unsupervised image enhancement module is designed that effectively restores texture, structure and color information in visible images by several non-reference loss functions. Then, an intensity image fusion module is devised to integrate the enhanced visible image and the infrared image. Moreover, to improve the infrared salient object feature in the fused images, we propose an infrared bilateral-guided salience map embedding into the fusion loss functions. Extensive experiments demonstrate that our method outperforms state-of-the-art infrared visible image fusion methods.

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection.

Infrared (IR) images containing rich spectral information are essential in many fields. Most RGB-IR transfer work currently relies on conditional generative models to learn and train IR images for specific devices and scenes. However, these models only establish an empirical mapping relationship between RGB and IR images in a single dataset, which cannot achieve the multi-scene and multi-band (0.7–3 $\mathsf{\mu }$m and 8–15 $\mathsf{\mu }$m) transfer task. To address this challenge, we propose VQ-InfraTrans, a comprehensive framework for transferring images from the visible spectrum to the infrared spectrum. Our framework incorporates a multi-mode approach to RGB-IR image transferring, encompassing both unconditional and conditional transfers, achieving diverse and flexible image transformations. Instead of training individual models for each specific condition or dataset, we propose a two-stage transfer framework that integrates diverse requirements into a unified model that utilizes a composite encoder–decoder based on VQ-GAN, and a multi-path transformer to translate multi-modal images from RGB to infrared. To address the issue of significant errors in transferring specific targets due to their radiance, we have developed a hybrid editing module to precisely map spectral transfer information for specific local targets. The qualitative and quantitative comparisons conducted in this work reveal substantial enhancements compared to prior algorithms, as the objective evaluation metric SSIM (structural similarity index) was improved by 2.24% and the PSNR (peak signal-to-noise ratio) was improved by 2.71%.

Multimodal feature alignment is a key challenge in referring remote sensing image segmentation (RRSIS). The complex spatial relationships and multi-scale targets in remote sensing images call for efficient cross-modal mapping and fine-grained feature alignment. Existing approaches typically rely on cross-attention for multimodal fusion, which increases model complexity. To address this, we introduce the concept of prompt learning in RRSIS and propose a parameter-efficient multimodal prompt-guided bidirectional fusion (MPBF) architecture. MPBF combines both early and late fusion strategies. In the early fusion stage, it conducts the deep fusion of linguistic and visual features through cross-modal prompt coupling. In the late fusion stage, to handle the multi-scale nature of remote sensing targets, a scale refinement module is proposed to capture diverse scale representations, and a vision–language alignment module is employed for pixel-level multimodal semantic associations. Comparative experiments and ablation studies on a public dataset demonstrate that MPBF significantly outperformed existing state-of-the-art methods with relatively small computational overhead, highlighting its effectiveness and efficiency for RRSIS. Further application experiments on a custom dataset confirm the method’s practicality and robustness in real-world scenarios.

The bionic polarization compass is a fascinating subject in the navigation domain. Existing polarization navigation models are primarily based on Rayleigh scattering theory, which is applicable to high-altitude, dry, and clear weather conditions. In most scenarios, it is difficult to meet such ideal clear conditions. This paper proposes a bionic navigation method based on atmospheric polarization optimization to improve heading accuracy under non-ideal clear conditions. A signal model under non-ideal clear conditions was firstly established to introduce disturbances of aerosols and other particles into the raw signal function acquired by a camera. Then, an energy functional optimization model was constructed to eliminate the disturbances caused by large particle scattering and restore the original sky polarization pattern. Subsequently, the heading angle was calculated using astronomical data, enhancing accuracy under non-ideal conditions. Finally, we constructed a polarization compass system and conducted field experiments. The results demonstrate that the proposed algorithm effectively mitigates the impact of scattering from aerosols and other particles, reducing the heading angle error to within 2° under sunny, cloudy, overcast and sandy conditions.