HDR¶
HDRCNN (Siggraph Asia 2017)¶
HDR image reconstruction from a single exposure using deep CNNs (Siggraph Asia 2017)
A hybrid dynamic range autoencoder that is tailored to operate on LDR input data and output HDR images. It utilizes HDR specific transfer-learning, skip-connections, color space and loss function
Deep Reverse Tone Mapping (2017)¶
Deep Reverse Tone Mapping input:
- LDR image
- Fully CNNs: up-exposure model & down-exposure model → infer bracketed image
merge up-exposed and down-exposed image
HDR image database is for creating bracketed LDR image by simulating cameras
DeepHDR (ECCV 2018)¶
https://arxiv.org/abs/1711.08937 Project | Tensorflow
This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. (without optical flows)
hdrnet (Siggraph 2017)¶
Deep Photo Enhancer (CVPR 2018)¶
Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
Tensorflow implementation
trackle the problem with two-ways GAN whose structure similar to CycleGAN
individual batch normalization layers in generators with raw/ generated source
iBN: without iBN, color will be broken
Global features: extract iin layer 5, FC until 1x1x128, duplicated to 32x32x128
adaptive WGAN (A-WGAN)
Limitations
amplify noise if the input is very dark and contains a significant amount of noise.
In addition, since some HDR images for training are products of tone mapping, our model could suffer from halo artifacts inherited from tone mapping for some images
ExpandNet (Eurographics 2018)¶
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content test on video HDR: Black might blended to some color, probably because of the loss. If setting lambda to zero, black become pure black without details. Is it really a good choise to use linear RGB? It seems that dark color is difficult to train because it is too small on linear domain.