Learning Continuous Exposure Value Representations for Single-Image HDR
  Reconstruction
Su-Kai Chen Hung-Lin Yen Yu-Lun Liu Min-Hung Chen Hou-Ning Hu Wen-Hsiao Peng Yen-Yu Lin
Deep learning is commonly used to reconstruct HDR images from LDR images. LDR
stack-based methods are used for single-image HDR reconstruction, generating an
HDR image from a deep learning-generated LDR stack. However, current methods
generate the stack with predetermined exposure values (EVs), which may limit
the quality of HDR reconstruction. To address this, we propose the continuous
exposure value representation (CEVR), which uses an implicit function to
generate LDR images with arbitrary EVs, including those unseen during training.
Our approach generates a continuous stack with more images containing diverse
EVs, significantly improving HDR reconstruction. We use a cycle training
strategy to supervise the model in generating continuous EV LDR images without
corresponding ground truths. Our CEVR model outperforms existing methods, as
demonstrated by experimental results.