HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
CoRR(2024)
Abstract
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.
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