  Multi-frame high dynamic range (HDR) imaging aims to reconstruct ghost-free
images with photo-realistic details from content-complementary but spatially
misaligned low dynamic range (LDR) images. Existing HDR algorithms are prone to
producing ghosting artifacts as their methods fail to capture long-range
dependencies between LDR frames with large motion in dynamic scenes. To address
this issue, we propose a novel image fusion transformer, referred to as IFT,
which presents a fast global patch searching (FGPS) module followed by a
self-cross fusion module (SCF) for ghost-free HDR imaging. The FGPS searches
the patches from supporting frames that have the closest dependency to each
patch of the reference frame for long-range dependency modeling, while the SCF
conducts intra-frame and inter-frame feature fusion on the patches obtained by
the FGPS with linear complexity to input resolution. By matching similar
patches between frames, objects with large motion ranges in dynamic scenes can
be aligned, which can effectively alleviate the generation of artifacts. In
addition, the proposed FGPS and SCF can be integrated into various deep HDR
methods as efficient plug-in modules. Extensive experiments on multiple
benchmarks show that our method achieves state-of-the-art performance both
quantitatively and qualitatively.
