Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Related tags

Deep Learning HEP
Overview

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior.

The code will release soon.

Implementation

  • Python3
  • PyTorch>=1.0
  • NVIDIA GPU+CUDA

Guidance

The code will release soon.

Paper Summary

HEP consists of two stages, Light Up Module (LUM) and Noise Disentanglement Module (LUM) Main Pipeline

Representative Visual Results

LOL SCIE

README waits for updated, more visual results will release soon

Citation

if you find this repo is helpful, please cite

@misc{zhang2021unsupervised,
      title={Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior}, 
      author={Feng Zhang and Yuanjie Shao and Yishi Sun and Kai Zhu and Changxin Gao and Nong Sang},
      year={2021},
      eprint={2112.01766},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
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Comments
  • Question about using reflectance map as the output

    Question about using reflectance map as the output

    Hi, the HE prior is awesome, but I am kind of confused about directly using (denoised) reflectance map as the final output, without combined with the (enhanced) illumination map. In most low-light enhancement methods, to my knowledge, they separately enhance the reflectance map and the illumination map, and multiply them to construct the final enhanced image. I am wondering if it is a intended design? Will the output be over-brighten?

    opened by QiuJueqin 1
  • Dimension mismatch for x2 and x5 in LUM_model.py

    Dimension mismatch for x2 and x5 in LUM_model.py

    Traceback (most recent call last): File "LUM_train.py", line 156, in main() File "LUM_train.py", line 134, in main r_x, i_x = light(val_x) File "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/mnt/batch/tasks/shared/LS_root/mounts/clusters/deepakpanda4/code/Users/deepakpanda/HEP/models/LUM_model.py", line 48, in forward cat5 = torch.cat((x5, x2), dim=1) RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 766 but got size 765 for tensor number 1 in the list.

    opened by deepakkupanda 1
  • Normalization in VGG preprocess

    Normalization in VGG preprocess

    Hi, as stated in torchvision page, the input to torchvision's pretrained VGG should be RGB format and normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

    However in your code, RGB image is converted to BGR format and normalized by mean=[103.939, 116.779, 123.680] and std=[1.0, 1.0, 1.0]:

    https://github.com/fengzhang427/HEP/blob/c0188bb3c69f2d5f8842f6ee2987b6fa5eb46241/utils.py#L219-L229

    Should this be fixed?

    opened by QiuJueqin 1
Owner
FengZhang
Ph.D. Candidates.
FengZhang
GitHub
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