Infrared and visible sensors offer distinct advantages owing to their disparate imaging principles. Visible images can provide high spatial resolution details under well-lighting conditions. However, the principle of converting natural light refraction into electrical signals by sensors significantly reduces the imaging quality under low light conditions. In contrast, infrared images are capable of capturing information about thermal targets in a scene, and the sensor is not affected by weak light conditions such as overexpose nighttime, rain and fog. However, there are significant differences between infrared and visible images, and it remains challenging to extract features sufficiently to generate visually appealing images. In the past decades, numerous methods for fusing infrared and visible images aim to enhance visual quality by improving feature extraction or fusion strategies. Initially, researchers employed various predefined transforms and hierarchies for decomposition and reconstruction. In this way, they designed various kinds of traditional fusion methods, such as multi-scale transform-based methods, sparse representation-based methods and subspace-based methods. However, traditional methods rely excessively on manual design of feature extraction and fusion strategies, which leads to degraded fusion performance. With the powerful feature representation capability of the network, deep learning is applied in the field of infrared and visible image fusion (IVIF). Through the training of the network, image fusion is transformed into an inference optimization problem. Subsequently, fusion methods based on CNN, GAN and Transformer architectures have been proposed successively and more attractive fusion results have been achieved.
However, both traditional and deep learning-based IVIF methods prioritize enhancing visual effects and fusion quality, neglecting how to serve the downstream tasks, whereas advanced visual tasks such as object detection tasks are key to computer vision applications. To enhance the efficacy of fused images for downstream tasks, researchers integrate semantic segmentation and target detection labels into imaging data and refine fusion networks through loss backpropagation. This has been somewhat successful in facilitating the semantic representation of fused images. In subsequent work, an increasing number of novel networks have been proposed, and the task has gradually evolved from semantic segmentation to encompass object recognition tasks. This joint approach enables the generation of semantically richer fused images for IVIF, while OD contributes valuable semantic information to improve IVIF.
Despite these advancements, these practices fail to take full advantage of the feature information in advanced vision tasks. Additionally, it is important to note that there are significant differences in the features required for the fusion and detection tasks. In this case, directly using the features of the detection network for the fusion task could potentially lead to a degradation in the quality of the fused image. consequently, striking a balance in the network training process and fully leveraging features from both modalities and tasks remains a significant challenge.
International Awards on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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