3 research outputs found
End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result
The general aim of multi-focus image fusion is to gather focused regions of
different images to generate a unique all-in-focus fused image. Deep learning
based methods become the mainstream of image fusion by virtue of its powerful
feature representation ability. However, most of the existing deep learning
structures failed to balance fusion quality and end-to-end implementation
convenience. End-to-end decoder design often leads to unrealistic result
because of its non-linear mapping mechanism. On the other hand, generating an
intermediate decision map achieves better quality for the fused image, but
relies on the rectification with empirical post-processing parameter choices.
In this work, to handle the requirements of both output image quality and
comprehensive simplicity of structure implementation, we propose a cascade
network to simultaneously generate decision map and fused result with an
end-to-end training procedure. It avoids the dependence on empirical
post-processing methods in the inference stage. To improve the fusion quality,
we introduce a gradient aware loss function to preserve gradient information in
output fused image. In addition, we design a decision calibration strategy to
decrease the time consumption in the application of multiple images fusion.
Extensive experiments are conducted to compare with 19 different
state-of-the-art multi-focus image fusion structures with 6 assessment metrics.
The results prove that our designed structure can generally ameliorate the
output fused image quality, while implementation efficiency increases over 30\%
for multiple images fusion.Comment: repor