HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image
  Translation
Hrishav Bakul Barua Ganesh Krishnasamy KokSheik Wong Abhinav Dhall Kalin Stefanov
High Dynamic Range (HDR) imaging aims to replicate the high visual quality
and clarity of real-world scenes. Due to the high costs associated with HDR
imaging, the literature offers various data-driven methods for HDR image
reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation
of these approaches is missing details in regions of the reconstructed HDR
images, which are over- or under-exposed in the input LDR images. To this end,
we propose a simple and effective method, HistoHDR-Net, to recover the fine
details (e.g., color, contrast, saturation, and brightness) of HDR images via a
fusion-based approach utilizing histogram-equalized LDR images along with
self-attention guidance. Our experiments demonstrate the efficacy of the
proposed approach over the state-of-art methods.