  In recent years, deep learning-based methods have achieved remarkable
progress in multi-exposure image fusion. However, existing methods rely on
aligned image pairs, inevitably generating artifacts when faced with device
shaking in real-world scenarios. Moreover, these learning-based methods are
built on handcrafted architectures and operations by increasing network depth
or width, neglecting different exposure characteristics. As a result, these
direct cascaded architectures with redundant parameters fail to achieve highly
effective inference time and lead to massive computation. To alleviate these
issues, in this paper, we propose a search-based paradigm, involving
self-alignment and detail repletion modules for robust multi-exposure image
fusion. By utilizing scene relighting and deformable convolutions, the
self-alignment module can accurately align images despite camera movement.
Furthermore, by imposing a hardware-sensitive constraint, we introduce neural
architecture search to discover compact and efficient networks, investigating
effective feature representation for fusion. We realize the state-of-the-art
performance in comparison to various competitive schemes, yielding a 4.02% and
29.34% improvement in PSNR for general and misaligned scenarios, respectively,
while reducing inference time by 68.1%. The source code will be available at
https://github.com/LiuZhu-CV/CRMEF.
