Vol. 53, Issue 1, pp. 49-64

Vol. 53 Issue 1 pp. 49-64

Infrared and visible image fusion with deep wavelet-dense network

Yanling Chen, Lianglun Cheng, Heng Wu, Ziyang Chen, Feng Li


infrared image, image fusion, image processing, infrared image enhancement


We propose a high-quality infrared and visible image fusion method based on a deep wavelet-dense network (WT-DenseNet). The WT-DenseNet includes three network layers, the hybrid feature extraction layer, fusion layer, and image reconstruction layer. The hybrid feature extraction layer is composed of a wavelet and dense network. The wavelet network decomposes the feature map of the visible and infrared images into low-frequency and high-frequency components, respectively. The dense network extracts the salient features. A fusion layer is designed to integrate low-frequency and salient features. Finally, the fusion images are outputted by an image reconstruction layer. The experimental results demonstrate that the proposed method can realize high-quality infrared and visible image fusions, and the performance of the proposed method is better than that of the six recently published fusion methods in terms of contrast and detail performance.

Vol. 53
Issue 1
pp. 49-64

3.81 MB
OPTICA APPLICATA - a quarterly of the Wrocław University of Science and Technology, Faculty of Fundamental Problems of Technology