End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result

TL;DR

This paper introduces a novel end-to-end cascade neural network that simultaneously generates a decision map and fused image for multi-focus image fusion, improving quality and efficiency without relying on empirical post-processing.

Contribution

It proposes a unified cascade network with a gradient-aware loss and decision calibration, enhancing fusion quality and reducing processing time compared to existing methods.

Findings

Outperforms 19 state-of-the-art fusion methods across 6 metrics.

Achieves over 30% efficiency improvement in multiple images fusion.

Produces higher quality fused images with better gradient preservation.

Abstract

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.