In the first stage, we pretrain a Saturated Mask. AutoEncoder (SMAE), which learns the representation of. HDR features to generate content of saturated regions ...
Apr 14, 2023 · In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR.
This work proposes a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR, and designs a Saturated Mask ...
In the first stage, we pretrain a Saturated Mask. AutoEncoder (SMAE), which learns the representation of. HDR features to generate content of saturated regions ...
In the first stage, we propose a multi-scale Transformer model based on self-supervised learning with a saturated-masked autoencoder to make it capable of ...
Sep 6, 2024 · Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural ...
In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR.
[7][8][9][10] The approach produces desired HDR images in static scenes but introduces artifacts such as ghosting and blurring in dynamic scenes due to ...
In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Few-Shot Learning · Pseudo ...
CVPR-2023. SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders, CVPR-2023, Kalantari, Hu, Few shot HDR. Joint HDR Denoising and ...