In this paper, a novel fusion network for infrared and visible images is proposed, named Image Pair Learning Fusion Network (IPLF). At present, most of the released deep learning-based fusion models are trained using unsupervised learning. This learning method lacks ground truth and cannot guide network model learning in a targeted manner. First, we propose the use of supervised learning to guide the training of fusion models (IPLF). The generated image pairs are used as ground truth. Second, we propose a model learning strategy using paired images. This learning strategy enhances the complementary constraints of the network model so that the final fused image possesses the rich features of both infrared and visible images. Third, we designed the structure measurement loss function and the edge preservation loss function to ensure that the generated fusion image has rich edge information and comfortable visual effects. In addition, we have introduced a spatial attention module, which can make the final fusion image highlight the target information. Finally, the size of the convolution kernel we use in the first convolution block is $7\ \times\ 7$ , the main purpose is to increase the receptive field. Experiments on the TNO and CVC-14 datasets prove that our proposed method is superior to the existing state-of-the-art methods in terms of qualitative and quantitative evaluation. The source code can be found in https://github.com/depeng6/IPLF .

When traditional super-resolution reconstruction methods are applied to infrared thermal images, they often ignore the problem of poor image quality caused by the imaging mechanism, which makes it difficult to obtain high-quality reconstruction results even with the training of simulated degraded inverse processes. To address these issues, we proposed a thermal infrared image super-resolution reconstruction method based on multimodal sensor fusion, aiming to enhance the resolution of thermal infrared images and rely on multimodal sensor information to reconstruct high-frequency details in the images, thereby overcoming the limitations of imaging mechanisms. First, we designed a novel super-resolution reconstruction network, which consisted of primary feature encoding, super-resolution reconstruction, and high-frequency detail fusion subnetwork, to enhance the resolution of thermal infrared images and rely on multimodal sensor information to reconstruct high-frequency details in the images, thereby overcoming limitations of imaging mechanisms. We designed hierarchical dilated distillation modules and a cross-attention transformation module to extract and transmit image features, enhancing the network’s ability to express complex patterns. Then, we proposed a hybrid loss function to guide the network in extracting salient features from thermal infrared images and reference images while maintaining accurate thermal information. Finally, we proposed a learning strategy to ensure the high-quality super-resolution reconstruction performance of the network, even in the absence of reference images. Extensive experimental results show that the proposed method exhibits superior reconstruction image quality compared to other contrastive methods, demonstrating its effectiveness.

Image fusion operation is beneficial to many applications and is also one of the most common and critical computer vision challenges. The perfect infrared and visible image fusion results should include the important infrared targets while preserving visible textural detail information as much as possible. A novel infrared and visible image fusion framework is proposed for this purpose. In this paper, the proposed fusion network (MIFFuse) is an end-to-end, multi-level-based fusion network for infrared and visible images. The presented approach makes effective use of the intermediate convolution layer’s output features to preserve the primary image fusion information. We also build a cat_block to swap information between two paths to gain more sufficient information during the convolution steps. To reduce the model’s running time even further, the proposed method that reduces the number of feature channels while maintaining the accuracy of the fusion performance. Extensive experiments on the TNO and CVC-14 image fusion datasets show that our MIFFuse outperforms the other methods in terms of both subjective visual effects and quantitative metrics. Furthermore, MIFFuse is approximately twice as fast as the most recent state-of-the-art methods. Our code and models can be found at https://github.com/depeng6/MIFFuse .

RGB-thermal object detection harnesses complementary information from visible and thermal modalities to enhance detection robustness in challenging environments, particularly under low-light conditions. However, existing approaches suffer from limitations due to their heavy dependence on precisely registered data and insufficient handling of cross-modal distribution disparities. This paper presents RDCRNet, a novel framework incorporating a Cross-Modal Representation Model to effectively address these challenges. The proposed network features a Cross-Modal Feature Remapping Module that aligns modality distributions through statistical normalization and learnable correction parameters, significantly reducing feature discrepancies between modalities. A Cross-Modal Refinement and Interaction Module enables sophisticated bidirectional information exchange via trinity refinement for intra-modal context modeling and cross-attention mechanisms for unaligned feature fusion. Multiscale detection capability is enhanced through a Cross-Scale Feature Integration Module, improving detection performance across various object sizes. To overcome the inherent data scarcity in RGB-T detection, we introduce a self-supervised pretraining strategy that combines masked reconstruction with adversarial learning and semantic consistency loss, effectively leveraging both aligned and unaligned RGB-T samples. Extensive experiments demonstrate that RDCRNet achieves state-of-the-art performance on multiple benchmark datasets while maintaining high computational and storage efficiency, validating its superiority and practical effectiveness in real-world applications.