2 research outputs found
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
Recovering a high dynamic range (HDR) image from a single low dynamic range
(LDR) input image is challenging due to missing details in under-/over-exposed
regions caused by quantization and saturation of camera sensors. In contrast to
existing learning-based methods, our core idea is to incorporate the domain
knowledge of the LDR image formation pipeline into our model. We model the
HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2)
non-linear mapping from a camera response function, and (3) quantization. We
then propose to learn three specialized CNNs to reverse these steps. By
decomposing the problem into specific sub-tasks, we impose effective physical
constraints to facilitate the training of individual sub-networks. Finally, we
jointly fine-tune the entire model end-to-end to reduce error accumulation.
With extensive quantitative and qualitative experiments on diverse image
datasets, we demonstrate that the proposed method performs favorably against
state-of-the-art single-image HDR reconstruction algorithms.Comment: CVPR 2020. Project page:
https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code:
https://github.com/alex04072000/SingleHD