  In recent years, deep learning networks have made remarkable strides in the
domain of multi-exposure image fusion. Nonetheless, prevailing approaches often
involve directly feeding over-exposed and under-exposed images into the
network, which leads to the under-utilization of inherent information present
in the source images. Additionally, unsupervised techniques predominantly
employ rudimentary weighted summation for color channel processing, culminating
in an overall desaturated final image tone. To partially mitigate these issues,
this study proposes a gamma correction module specifically designed to fully
leverage latent information embedded within source images. Furthermore, a
modified transformer block, embracing with self-attention mechanisms, is
introduced to optimize the fusion process. Ultimately, a novel color
enhancement algorithm is presented to augment image saturation while preserving
intricate details. The source code is available at
https://github.com/ZhiyingDu/BHFMEF.
