Boosting HDR Image Reconstruction via Semantic Knowledge Transfer
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range
(LDR) images becomes challenging when the LDR images exhibit noticeable
degradation and missing content. Leveraging scene-specific semantic priors
offers a promising solution for restoring heavily degraded regions. However,
these priors are typically extracted from sRGB Standard Dynamic Range (SDR)
images, the domain/format gap poses a significant challenge when applying it to
HDR imaging. To address this issue, we propose a general framework that
transfers semantic knowledge derived from SDR domain via self-distillation to
boost existing HDR reconstruction. Specifically, the proposed framework first
introduces the Semantic Priors Guided Reconstruction Model (SPGRM), which
leverages SDR image semantic knowledge to address ill-posed problems in the
initial HDR reconstruction results. Subsequently, we leverage a
self-distillation mechanism that constrains the color and content information
with semantic knowledge, aligning the external outputs between the baseline and
SPGRM. Furthermore, to transfer the semantic knowledge of the internal
features, we utilize a semantic knowledge alignment module (SKAM) to fill the
missing semantic contents with the complementary masks. Extensive experiments
demonstrate that our method can significantly improve the HDR imaging quality
of existing methods.
