Name
Title | ||
|---|---|---|
DSG-Fusion: Infrared and visible image fusion via generative adversarial networks and guided filter |
Abstract | ||
|---|---|---|
The goal of infrared and visible image fusion is to generate an informative image which preserves texture details and infrared targets. Most generative adversarial network (GAN) based methods take a concatenation of two source images as the input, hence the extracted feature is insufficient for preserving background and detail information. To this end, we propose a novel GAN based fusion framework, termed as double-stream guided filter network (DSG-Fusion). Given the diverse modalities of infrared and visible images, the generator of DSG network extracts features of two images through two independent data flows. To address the problem of extracting representative background information and force the DSG network focus on details, we integrate guided filter into the generator to obtain base and detail layers of source images. The base layers are concatenated with the corresponding source images to extract deeper features, while detail layers participate in the fusion procedure directly. Thus, DSG-Fusion can extract texture and intensity information sufficiently, and more background and detail information are preserved. Furthermore, a DSG loss consisting of intensity and structural similarity (SSIM) is designed to impel the network to generate a better fused image. Extensive experimental results show that DSG-Fusion achieves better performance comparing with five representative methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.eswa.2022.116905 | Expert Systems with Applications |
Keywords | DocType | Volume |
Infrared and visible image fusion,Guided filter,Generative adversarial networks | Journal | 200 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 6 | |