Automatically translating chromaticity-free thermal infrared (TIR) images into realistic color visible (CV) images is of great significance for autonomous vehicles, emergency rescue, robot navigation, nighttime video surveillance, and many other fields. Most recent designs use end-to-end neural networks to translate TIR directly to CV; however, compared to these networks, TIR has low contrast and an unclear texture for CV translation. Thus, directly translating the TIR temperature value of only one channel to the RGB color value of three channels without adding additional constraints or semantic information does not handle the one-to-three mapping problem between different domains in a good way, causing the translated CV images not only to have blurred edges but also color confusion. As for the methodology of the work, considering that in the translation from TIR to CV the most important process is to map information from the temperature domain into the color domain, an improved CycleGAN (GMA-CycleGAN) is proposed in this work in order to translate TIR images to grayscale visible (GV) images. Although the two domains have different properties, the numerical mapping is one-to-one, which reduces the color confusion caused by one-to-three mapping when translating TIR to CV. Then, a GV-CV translation network is applied to obtain CV images. Since the process of decomposing GV images into CV images is carried out in the same domain, edge blurring can be avoided. To enhance the boundary gradient between the object (pedestrian and vehicle) and the background, a mask attention module based on the TIR temperature mask and the CV semantic mask is designed without increasing the network parameters, and it is added to the feature encoding and decoding convolution layers of the CycleGAN generator. Moreover, a perceptual loss term is applied to the original CycleGAN loss function to bring the translated images closer to the real images regarding the space feature. In order to verify the effectiveness of the proposed method, the FLIR dataset is used for experiments, and the obtained results show that, compared to the state-of-the-art model, the subjective quality of the translated CV images obtained by the proposed method is better, as the objective evaluation metric FID (Fréchet inception distance) is reduced by 2.42 and the PSNR (peak signal-to-noise ratio) is improved by 1.43.

Nighttime Thermal InfraRed (NTIR) image colorization, also known as the translation of NTIR images into Daytime Color Visible (DCV) images, can facilitate human and intelligent system perception of nighttime scenes under weak lighting conditions. End-to-end neural networks have been used to learn the mapping relationship between temperature and color domains, and translate NTIR images with one channel into DCV images with three channels. However, this mapping relationship is an ill-posed problem with multiple solutions without constraints, resulting in blurred edges, color disorder, and semantic errors. To solve this problem, an NTIR2DCV method that includes two steps is proposed: firstly, fuse Nighttime Color Visible (NCV) images with NTIR images based on an Illumination-Aware, Multilevel Decomposition Latent Low-Rank Representation (IA-MDLatLRR) method, which considers the differences in illumination conditions during image fusion and adjusts the fusion strategy of MDLatLRR accordingly to suppress the adverse effects of nighttime lights; secondly, translate the Nighttime Fused (NF) image to DCV image based on HyperDimensional Computing Generative Adversarial Network (HDC-GAN), which ensures feature-level semantic consistency between the source image (NF image) and the translated image (DCV image) without creating semantic label maps. Extensive comparative experiments and the evaluation metrics values show that the proposed algorithms perform better than other State-Of-The-Art (SOTA) image fusion and translation methods, such as FID and KID, which decreased by 14.1 and 18.9, respectively.

The development of a cooperative pursuit strategy for capturing escaping criminals or dangerous animals in urban public safety emergencies is becoming increasingly in demand. An ideal strategy should consider both the encirclement needed to prevent criminals from evading and the distance that pursuers need to move. This article proposes a fine-grained navigation network-based cooperative hunting (FINNCH) method. A fine-grained navigation network is created to provide detailed information about the traversability in urban areas. Three interaction rules inspired by biological behaviors in nature are introduced to achieve dynamic cooperation between pursuers. An heuristic search strategy is used to guide the pursuers toward potentially good directions, which consequently reduces the search effort. Two spatial constraints, namely, the direction centrality constraint (DCC) and encirclement distance constraint (EDC), are then constructed to evenly distribute the pursuers around the evader. Several experiments are conducted to evaluate the effectiveness and efficiency of the proposed method. The results show that FINNCH can provide navigation for multiple pursuers in complex urban environments comprised of roads, meadows, trees, water, and buildings. These findings point to the promising future of FINNCH for practical applications.