NIR Fusion

NIR fusion combines visible and near-infrared (NIR) images to leverage their complementary spectral information, improving image quality and extracting more comprehensive scene data. Current research focuses on developing advanced algorithms, including those based on deep learning (e.g., convolutional neural networks) and sparse representation techniques, to address challenges like noise, artifacts, and color distortion in fused images. These advancements are particularly relevant for applications such as low-light imaging and autonomous driving, where enhanced scene understanding is crucial for improved safety and performance. The development of robust and efficient NIR fusion methods is driving progress in various fields requiring high-quality image data from diverse spectral ranges.