  Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes
has recently been extensively studied by exploiting Deep Neural Networks
(DNNs). Most DNNs-based methods require a large amount of training data with
ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging
aims to generate satisfactory images with limited data. However, it is
difficult for modern DNNs to avoid overfitting when trained on only a few
images. In this work, we propose a novel semi-supervised approach to realize
few-shot HDR imaging via two stages of training, called SSHDR. Unlikely
previous methods, directly recovering content and removing ghosts
simultaneously, which is hard to achieve optimum, we first generate content of
saturated regions with a self-supervised mechanism and then address ghosts via
an iterative semi-supervised learning framework. Concretely, considering that
saturated regions can be regarded as masking Low Dynamic Range (LDR) input
regions, we design a Saturated Mask AutoEncoder (SMAE) to learn a robust
feature representation and reconstruct a non-saturated HDR image. We also
propose an adaptive pseudo-label selection strategy to pick high-quality HDR
pseudo-labels in the second stage to avoid the effect of mislabeled samples.
Experiments demonstrate that SSHDR outperforms state-of-the-art methods
quantitatively and qualitatively within and across different datasets,
achieving appealing HDR visualization with few labeled samples.
