DePF: A Novel Fusion Approach based on Decomposition Pooling for Infrared and Visible Images

Infrared and visible image fusion aims to generate synthetic images simultaneously containing salient features and rich texture details, which can be used to boost downstream tasks. However, existing fusion methods are suffering from the issues of texture loss and edge information deficiency, which result in suboptimal fusion results. Meanwhile, the straight-forward up-sampling operator can not well preserve the source information from multi-scale features. To address these issues, a novel fusion network based on the decomposition pooling (de-pooling) manner is proposed, termed as DePF. Specifically, a de-pooling based encoder is designed to extract multi-scale image and detail features of source images at the same time. In addition, the spatial attention model is used to aggregate these salient features. After that, the fused features will be reconstructed by the decoder, in which the up-sampling operator is replaced by the de-pooling reversed operation. Different from the common max-pooling technique, image features after the de-pooling layer can retain abundant details information, which is benefit to the fusion process. In this case, rich texture information and multi-scale information are maintained during the reconstruction phase. The experimental results demonstrate that the proposed method exhibits superior fusion performance over the state-of-the-arts on multiple image fusion benchmarks.