Conditional Controllable Image Fusion

TL;DR

This paper introduces a conditional controllable image fusion framework that leverages denoising diffusion models to adaptively fuse images without specific training, enabling flexible and scenario-aware image synthesis.

Contribution

The proposed CCF framework uses adaptive constraints with pre-trained diffusion models to achieve general, training-free image fusion across diverse scenarios.

Findings

Effective in various fusion scenarios

No additional training required

Outperforms existing methods

Abstract

Image fusion aims to integrate complementary information from multiple input images acquired through various sources to synthesize a new fused image. Existing methods usually employ distinct constraint designs tailored to specific scenes, forming fixed fusion paradigms. However, this data-driven fusion approach is challenging to deploy in varying scenarios, especially in rapidly changing environments. To address this issue, we propose a conditional controllable fusion (CCF) framework for general image fusion tasks without specific training. Due to the dynamic differences of different samples, our CCF employs specific fusion constraints for each individual in practice. Given the powerful generative capabilities of the denoising diffusion model, we first inject the specific constraints into the pre-trained DDPM as adaptive fusion conditions. The appropriate conditions are dynamically selected to ensure the fusion process remains responsive to the specific requirements in each reverse diffusion stage. Thus, CCF enables conditionally calibrating the fused images step by step. Extensive experiments validate our effectiveness in general fusion tasks across diverse scenarios against the competing methods without additional training.