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.

Microgrid polarimeters have large advantages over conventional polarimeters because of the snapshot nature and because they have no moving parts. However, they also suffer from several error sources, such as fixed pattern noise (FPN), photon response nonuniformity (PRNU), pixel cross talk, and instantaneous field-of-view (IFOV) error. A characterization method is proposed to improve the measurement accuracy in visible waveband. We first calibrate the camera with uniform illumination so that the response of the sensor is uniform over the entire field of view without IFOV error. Then a spline interpolation method is implemented to minimize IFOV error. Experimental results show the proposed method can effectively minimize the FPN and PRNU.

Vision-based localization is a cost-effective and thus attractive solution for many intelligent mobile platforms. However, its accuracy and especially robustness still suffer from low illumination conditions, illumination changes, and aggressive motion. Event-based cameras are bio-inspired visual sensors that perform well in HDR conditions and have high temporal resolution, and thus provide an interesting alternative in such challenging scenarios. While purely event-based solutions currently do not yet produce satisfying mapping results, the present work demonstrates the feasibility of purely event-based tracking if an alternative sensor is permitted for mapping. The method relies on geometric 3D-2D registration of semi-dense maps and events, and achieves highly reliable and accurate cross-modal tracking results. Practically relevant scenarios are given by depth camera-supported tracking or map-based localization with a semi-dense map prior created by a regular image-based visual SLAM or structure-from-motion system. Conventional edge-based 3D-2D alignment is extended by a novel polarity-aware registration that makes use of signed time-surface maps (STSM) obtained from event streams. We furthermore introduce a novel culling strategy for occluded points. Both modifications increase the speed of the tracker and its robustness against occlusions or large view-point variations. The approach is validated on many real datasets covering the above-mentioned challenging conditions, and compared against similar solutions realised with regular cameras.

Event cameras provide high temporal resolution and high dynamic range and have application prospects in areas such as visual navigation, robotics, and image reconstruction. Event cameras output an asynchronous stream of address events that can be highly polluted by noise due to lighting and motion conditions. In order to better analyze and process noise with different spatiotemporal correlation, we introduce a method using an event address map (EAM) representation and classify event noise into background noise, near-edge noise, and recessive noise. In addition, since near-edge noise and recessive noise have a closer spatiotemporal correlation with real events, they are difficult to suppress with current denoising methods. Accordingly, we propose an adaptive EAM denoising method by adjusting the time window to form the EAM by measuring the number of events after pre-denoising and performing median filtering and opening. The method can better adapt to scenes of different motions to process near-edge noise, and can generate events to fill recessive noise. The output EAM can be directly used for subsequent process. Alternatively, to provide high temporal resolution, we restore the stream of address events from the output EAM. To suitably evaluate event denoising performance on real-world data, we introduce the average local event variance and amount of recessive noise, and the proposed method achieves improvements of 24.94% and 72.79%, respectively, compared with conventional denoising.

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.

As a super-resolution imaging method, compressive imaging (CI) has become a research hotspot in recent years. However, most researchers focus on the visible light and near-infrared regime. Further, most experimental studies are confined to a single-pixel camera, and there are comparatively few reports of the experimental studies about CI in the field of focal plane array-based (FPA) CI in the medium-wave infrared (MWIR). This paper derives the system model for an FPA CI system, describes the generation process of DMD masks, and modifies the block-based compressive sensing algorithm to be applicable to the FPA CI system. Based on the actual FPA CI system in MWIR, high-resolution MWIR images are obtained from a low-resolution MWIR sensor, realizing 16 times MWIR image super-resolution. However, imaging quality is not as ideal as that in visible light FPA CI system because of the particularities of MWIR. We analyze the particularities of an FPA CI system in MWIR and provide suggestions on how to solve them to improve performance. This work could provide guidance for researchers to build an experimental FPA CI system in MWIR.