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Infrared (IR) images inherently face the dual challenges of noise contamination and reduced contrast. However, existing image enhancement methods often overlook the intrinsic correlations between these factors—noise and low contrast—during multistage enhancement processes. Consequently, this oversight leads to a significant reduction in the fidelity of intricate details in IR images. In this article, we present a synergistic IR image enhancement network that simultaneously achieves denoising, contrast improvement, and detail preservation (DCDNet), which breaks down the overall enhancement process into more manageable steps. DCDNet is comprised of a detail awareness unit (DAU), a deep denoising prior (DDP), and a contrast improvement module (CIM). To maintain the details in the IR image, DAU is developed to extract the original detail feature information in DDP and integrate them into the CIM during contrast improvement to improve the final result. The detail information is derived from the encoder of the DDP, which focuses on denoising. The preserved detail features are subsequently incorporated into the decoder of the CIM, which is dedicated to enhancing contrast. Experimental results validate that our proposed approach surpasses other state-of-the-art methods for enhancing IR images in terms of the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF), and performance in downstream tasks. The code and dataset are publicly available at https://github.com/ChickenEating/IR-Enhancement.

The formation control of multi-agent systems has increasingly drawn attention for fulfilling numerous emerging applications and services. To achieve high-accuracy formation, the location awareness of all agents becomes an essential requirement. In this paper, we address the problem of network localization and formation control in a cooperative system with asynchronous agents. In particular, we formulate the joint localization and synchronization of agents as a statistical inference problem. The underlying probabilistic model is represented by a factor graph from which a message-passing algorithm is designed that computes approximations of the marginals of unknown variables, i.e. agents’ locations and clock offsets. Due to the Euclidean-norm operator involved in their computation no parametric closed-form expressions of the messages exist. As a compromise, implemented message-passing methods therefore resort to approximations of these messages. Conventional methods rely either on a first-order Taylor expansion of the norm operation or on non-parametric representations, e.g. by means particle filters (PFs), to compute such approximations. However, the former approach suffers from poor performance while the latter one experiences high complexity. The proposed message-passing algorithm in this paper is parametric. Specifically, it passes Gaussian messages that can be essentially obtained by suitably augmenting the factor graph and applying on it a hybrid method for combining belief propagation and variational message passing. Subsequently, the agents can exploit the estimated locations for determining the control policy. Two types of control policy are designed based on the optimization of a generalized cost function. We show that the proposed scheme enjoys a reduced complexity for multi-agent localization while achieving the desired formation with excellent accuracy.

The combination of infrared and visible videos aims to gather more comprehensive feature information from multiple sources and reach superior results on various practical tasks, such as detection and segmentation, over that of a single modality. However, most existing dual-modality object detection algorithms ignore the modal differences and fail to consider the correlation between feature extraction and fusion, which leads to incomplete extraction and inadequate fusion of dual-modality features. Hence, there raises an issue of how to preserve each unique modal feature and fully utilize the complementary infrared and visible information. Facing the above challenges, we propose a novel Differential Feature Awareness Network (DFANet) within antagonistic learning for infrared and visible object detection. The proposed model consists of an Antagonistic Feature Extraction with Divergence (AFED) module used to extract the differential infrared and visible features with unique information, and an Attention-based Differential Feature Fusion (ADFF) module used to fully fuse the extracted differential features. We conduct performance comparisons with existing state-of-the-art models on two benchmark datasets to represent the robustness and superiority of DFANet, and numerous ablation experiments to illustrate its effectiveness.