Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for
  Loss-free Multi-Exposure Image Fusion
Guanyao Wu Hongming Fu Jinyuan Liu Long Ma Xin Fan Risheng Liu
Multi-exposure image fusion (MEF) has emerged as a prominent solution to
address the limitations of digital imaging in representing varied exposure
levels. Despite its advancements, the field grapples with challenges, notably
the reliance on manual designs for network structures and loss functions, and
the constraints of utilizing simulated reference images as ground truths.
Consequently, current methodologies often suffer from color distortions and
exposure artifacts, further complicating the quest for authentic image
representation. In addressing these challenges, this paper presents a
Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which
introduces a bi-level optimization search scheme for automatic design of both
network structures and loss functions. More specifically, we harnesses a unique
dual research mechanism rooted in a novel weighted structure refinement
architecture search. Besides, a hybrid supervised contrast constraint
seamlessly guides and integrates with searching process, facilitating a more
adaptive and comprehensive search for optimal loss functions. We realize the
state-of-the-art performance in comparison to various competitive schemes,
yielding a 10.61% and 4.38% improvement in Visual Information Fidelity (VIF)
for general and no-reference scenarios, respectively, while providing results
with high contrast, rich details and colors.