Search

Fusion of information from infrared and visible images from different modalities has always been a hot research topic in the field of image processing. In order to solve the problem that existing fusion methods cannot fully utilize the deep information of images, this paper proposes a fusion algorithm DAE-Nest based on depth information extraction and enhancement. The algorithm consists of encoder-feature enhancement-fusion layer-decoder modules for unsupervised learning. At first, the improved coding structure is used to extract multi-scale features of the source image; secondly, the extracted features are data enhanced to fully utilize the deep image information; then, the enhanced features are fused according to the corresponding fusion strategy; finally, the decoder takes the input of fused features and reconstructs them into an information-rich fused image. DAE-Nest was subjected to corresponding fusion test experiments on the TNO and RoadScene data sets respectively and compared with 9 excellent fusion algorithms. In terms of subjective evaluation, DAE-Nest can make full use of the deep information in the source image, and the generated fusion image also conforms to the visual perception of the human eyes; in terms of objective evaluation indicators, DAE-Nest's average gradient and spatial frequency on the TNO data set, it achieved the best performance in the three evaluation indicators of structural similarity and structural similarity, and also achieved the best performance in the fusion quality evaluation index on the RoadScene data set. Therefore, the fusion method proposed in this paper has achieved excellent results both subjectively and objectively, and can effectively fuse infrared and visible images, laying a good foundation for subsequent visual tasks. • Design a deep multi-scale feature extraction structure to fully extract the deep scale features of the image. • Design feature enhancement structures to make full use of the deep scale features of the image. • Design end-to-end integration modules IRS (Inception ResNet Shallow) and IRD (Inception ResNet Deep). • The proposed method was evaluated on two test sets and good fusion results were achieved. [ABSTRACT FROM AUTHOR]