Then the base parts are fused by weighted-averaging.
Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken.
Then, the lowrank parts are fused by weighted-average strategy to preserve more contour information.
In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features.
We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts.
Deep learning is a rapidly developing approach in the field of infrared and visible image fusion.
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few.