HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
This addresses the issue of over- or under-exposed regions in HDR imaging for applications requiring high visual quality, but it appears incremental as it builds on existing data-driven methods.
The paper tackles the problem of missing details in HDR images reconstructed from LDR inputs by proposing HistoHDR-Net, a fusion-based method using histogram-equalized LDR images and self-attention guidance, which shows efficacy over state-of-the-art methods.
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.