| Title | Infrared and Visible Image Fusion Based on Multiscale Decomposition with Gaussian and Co-Occurrence Filters |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Hu, Yifang, He, Jianjun, Xu, Luyao |
| Conference Name | 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) |
| Keywords | artificial intelligence, co-occurrence filter, composability, compositionality, decomposition, filtering algorithms, Image edge detection, image fusion, Indexes, Information filters, infrared and visible light, Metrics, Pattern recognition, pubcrawl, smoothing methods |
| Abstract | The fusion of infrared and visible images using traditional multi-scale decomposition methods often leads to the loss of detailed information or the blurring of image edges, which is because the contour information and the detailed information within the contour cannot be retained simultaneously in the fusion process. To obtain high-quality fused images, a hybrid multi-scale decomposition fusion method using co-occurrence and Gaussian filters is proposed in this research. At first, by making full use of the smoothing effect of the Gaussian filter and edge protection characteristic of the co-occurrence filter, source images are decomposed into multiple hierarchical structures with different characteristics. Then, characteristics of sub-images at each level are analyzed, and the corresponding fusion rules are designed for images at different levels. At last, the final fused image obtained by combining fused sub-images of each level has rich scene information and clear infrared targets. Compared with several traditional multi-scale fusion algorithms, the proposed method has great advantages in some objective evaluation indexes. |
| DOI | 10.1109/PRAI53619.2021.9551089 |
| Citation Key | hu_infrared_2021 |
Infrared and Visible Image Fusion Based on Multiscale Decomposition with Gaussian and Co-Occurrence Filters