Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion
This work addresses image fusion for applications like surveillance or medical imaging, but it is incremental as it applies a new decomposition method to an existing task.
The authors tackled the problem of fusing visible and infrared images by proposing a deep decomposition network that extracts and fuses high- and low-frequency features, achieving better performance than state-of-the-art methods in subjective and objective evaluations.
Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. In this paper, we apply the image decomposition network to the image fusion task. We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. The two sets of feature images are fused using a specific fusion strategy to obtain fusion feature images. Finally, the feature images are reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, this method has achieved better performance in both subjective and objective evaluation.