/LIE-IQA-pytorch

The Pytorch Implementation of Quality Assessment for Enhanced Low-light Image

LIE-IQA

The Pytorch implementation of LIE-IQA.

You can get the MindSpore implementation here LIE-IQA-mindspore. It is worth noting that the MindSpore implementation of Image Intrinsic Decomposition (IID) Module is different from the Pytorch implementation , but there is not much difference in performance. Please refer to the specific code for details.

The code on Pytorch will be released soon.

Requirements

  • Python3
  • Pytorch 1.6
  • Torchvision
  • cuda 10.2

Quality Assessment for Enhanced Low-light Image

  • LIE-IQA Framework

  • Performance on LIE-IQA Dataset

  • Performance on General Scene IQA Dataset

    • LIVE, MDID, CSIQ

Quality Optimization for Low-light Image Enhancement

  • Optimization framework

  • Qualitative comparison of the quality optimization result

    • DALE[1]

  • Quantitative comparison of the quality optimization result

    • SSIM, NIQE, DISTS, hyperIQA and our LIE-IQA

References

[1]Kwon, Dokyeong, Guisik Kim, and Junseok Kwon. "DALE: Dark Region-Aware Low-light Image Enhancement." arXiv preprint arXiv:2008.12493 (2020).