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Practical Single-Image Super-Resolution Using Look-Up Table

bit compression

numpy의 타입이 np.int8이므로 정수의 capactiy는 8bit이다. 8bit를 bit compression을 할 때의 PSNR을 관찰한다.

  • 8bit와 [1~8]bit로 줄였을 때의 PSNR을 비교한다.

  • bit compression의 방법은 Trunc(Floor), Ceil, Round, Random 총 4가지 방식으로 실험하고, 결과를 비교한다.

    • Random의 경우, nearest even rounding 방법으로 구현했다.

    실험 결과

    image

Original Code

1. Training deep SR network

2. Transferring to LUT

  The resulting LUT: `./Model_S_x4_4bit_int8.npy`.
  - Title's rule:

2. Transferring To LUT의 결과, LUT가 생성된다. 이때, LUT는 2차원 정수 배열이고, Model_\*_x\*_\*bit_int*.npy의 형식으로 저장한다.

3. Testing using LUT

3. Testing using LUT를 통해, PSNR을 구한다.

Forked by original code

[Paper]

Dependency

  • Python 3.6
  • PyTorch
  • glob
  • numpy
  • pillow
  • tqdm
  • tensorboardx

BibTeX

@InProceedings{jo2021practical,
   author = {Jo, Younghyun and Kim, Seon Joo},
   title = {Practical Single-Image Super-Resolution Using Look-Up Table},
   booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   month = {June},
   year = {2021}
}

sr-lut's People

Contributors

yeonju52 avatar yhjo09 avatar

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