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.

Due to hardware limitations of Thermal InfraRed (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolutions (LRs) and blurred textures. Improving the spatial resolution of TIR images is of great significance for subsequent applications. Existing image Super-Resolution (SR) methods rely on High-Resolution (HR) ground truth for supervised training, resulting in limited generalization and a lack of mechanisms to preserve the physical consistency of thermal radiation. To address these two issues, this paper proposes an unsupervised super-resolution framework for UAV TIR imagery that integrates diffusion modeling with cross-modal texture transfer. The diffusion model enables stable reconstruction of the fundamental TIR structure without requiring high-resolution supervision, while multi-scale textures extracted from visible (VIS) imagery via Multi-Stage Decomposition based on Latent Low-Rank Representation (MS-DLatLRR) compensate for missing details. To suppress temperature distortions introduced by cross-modal texture transfer, a physics-guided constraint termed Prior-Informed Emissivity-Guided Coefficient Mapping (PI-EGCM) is incorporated. Emissivity-aware guidance maps constructed via semantic classification regulate texture transfer and preserve thermal radiation consistency. Experimental results demonstrate that the proposed method improves spatial resolution and perceptual quality while effectively maintaining temperature fidelity, achieving a balanced enhancement of structural detail and physical consistency.

Remote sensing images acquired by UAVs under nighttime or low-light conditions suffer from insufficient illumination, leading to degraded image quality, detail loss, and noise, which restrict their application in public security and disaster emergency scenarios. Although existing machine learning-based enhancement methods can recover part of the missing information, they often cause color distortion and texture inconsistency. This study proposes an improved low-light image enhancement method based on a Weakly Paired Image Dataset (WPID), combining the Hierarchical Deep Convolutional Generative Adversarial Network (HDCGAN) with a low-rank image fusion strategy to enhance the quality of low-light UAV remote sensing images. First, YCbCr color channel separation is applied to preserve color information from visible images. Then, a Low-Rank Representation Fusion Network (LRRNet) is employed to perform structure-aware fusion between thermal infrared (TIR) and visible images, thereby enabling effective preservation of structural details and realistic color appearance. Furthermore, a weakly paired training mechanism is incorporated into HDCGAN to enhance detail restoration and structural fidelity. To achieve objective evaluation, a structural consistency assessment framework is constructed based on semantic segmentation results from the Segment Anything Model (SAM). Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both visual quality and application-oriented evaluation metrics.

Remote sensing images acquired by UAVs under nighttime or low-illumination conditions suffer from insufficient illumination, leading to degraded image quality, detail loss, and noise, which restrict their application in public security and disaster emergency scenarios. Although existing machine learning-based enhancement methods can recover part of the missing information, they often cause color distortion and texture inconsistency. This study proposes an improved low-illumination image enhancement method based on a Weakly Paired Image Dataset (WPID), combining the Hierarchical Deep Convolutional Generative Adversarial Network (HDCGAN) with a low-rank image fusion strategy to enhance the quality of low-illumination UAV remote sensing images. First, YCbCr color channel separation is applied to preserve color information from visible images. Then, a Low-Rank Representation Fusion Network (LRRNet) is employed to perform structure-aware fusion between thermal infrared (TIR) and visible images, thereby enabling effective preservation of structural details and realistic color appearance. Furthermore, a weakly paired training mechanism is incorporated into HDCGAN to enhance detail restoration and structural fidelity. To achieve objective evaluation, a structural consistency assessment framework is constructed based on semantic segmentation results from the Segment Anything Model (SAM). Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both visual quality and application-oriented evaluation metrics.

Due to hardware limitations of Thermal Infrared (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolution (LR) and blurred textures. Improving the spatial resolution of TIR images is of great significance for subsequent applications. Existing image Super-Resolution (SR) methods rely on High-Resolution (HR) ground truth for supervised training, resulting in poor general-ization ability. They also lack constraints on the temperature information of TIR imag-es, failing to maintain the consistency of temperature information reconstruction. To address these two issues, this paper proposes a UAV TIR image SR method based on diffusion models and cross-modal texture transfer, which introduces HR information from Visible (VIS) images into TIR images. Firstly, a Multi-Stage Decomposition Latent Low-Rank Representation (MS-DLatLRR) method is adopted to extract multi-scale de-tailed textures from VIS images. Secondly, prior information of object thermal radia-tion is introduced, and combined with the segmentation map of VIS images, a guided coefficient map for VIS multi-scale detailed texture transfer is constructed to provide constraints for temperature consistency during the cross-modal texture transfer pro-cess. Finally, the multi-scale detailed textures and the guided coefficient map are in-troduced into a diffusion model (MP-DDNM) for SR processing of TIR images. Exper-imental results show that compared with existing methods, the proposed method im-proves the resolution of UAV TIR images while maintaining the consistency of tem-perature information as much as possible.