LeRF-PyTorch

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[CVPR 2023 & T-PAMI 2025] Learning Resampling Function for Adaptive and Efficient Image Interpolation

LeRF-PyTorch 최신버젼 다운로드

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## [CVPR 2023] Learning Steerable Function for Efficient Image Resampling

## [T-PAMI 2025] LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation

This is the authors' reimplementation code in PyTorch.

[Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_Steerable_Function_for_Efficient_Image_Resampling_CVPR_2023_paper.pdf) | [Supp.](https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_Steerable_Function_CVPR_2023_supplemental.pdf) | [T-PAMI Paper](https://ieeexplore.ieee.org/document/11027639) | [Poster](https://lerf.pages.dev/static/LeRF-Poster_CVPR23.pdf) | [Project Page](https://lerf.pages.dev/) | [Intro Video](https://www.youtube.com/watch?v=6Sgnq2AD5yw)

## News

2025.06 The extended version of LeRF is accepted by [IEEE T-PAMI](https://ieeexplore.ieee.org/document/11027639).

2025.05 My Ph.D. advisor, Prof. Zhiwei Xiong, gave a [talk (in Chinese)](https://ccig.csig.org.cn/2025/6874/list.html#:~:text=%E6%8A%A5%E5%91%8A%E9%A2%98%E7%9B%AE%EF%BC%9A%20%E5%9F%BA%E4%BA%8E%E5%8F%AF%E5%AD%A6%E4%B9%A0%E6%9F%A5%E6%89%BE%E8%A1%A8%E7%9A%84%E9%AB%98%E6%95%88%E5%9B%BE%E5%83%8F%E5%A4%84%E7%90%86) on learned LUTs at Chinese Congress on Image and Graphics (CCIG) 2025.

2024.12 Our new work, [In-Loop Filtering via Trained Look-Up Tables(ILF-LUT)](https://ieeexplore.ieee.org/abstract/document/10849824) has been accepted by VCIP 2024. ILF-LUT extends learned LUTs to video coding, offering a way to integrate learned components into the video codec pipeline.

2024.07 Extended version of LeRF is available on [arxiv](https://arxiv.org/abs/2407.09935).

2024.07 A reimplementation of LeRF training code is open sourced here in PyTorch.

2024.06 Our new work, [Diagonal-First Compression for LUT(DFC)](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Look-Up_Table_Compression_for_Efficient_Image_Restoration_CVPR_2024_paper.html) has been presented as a highlight paper at CVPR 2024.

2024.05 The extended version of MuLUT, DNN-of-LUT, is accepted by [IEEE T-PAMI](https://ieeexplore.ieee.org/document/10530442).

2023.06 LeRF evaluation code is open sourced in [MindSpore](https://gitee.com/mindspore/models/tree/master/research/cv/lerf).

2023.02 Conference version of LeRF is accepted by CVPR 2023.

2022.10 MuLUT is open sourced in [PyTorch](https://github.com/ddlee-cn/MuLUT).

2022.07 Conference version of MuLUT is accepted by ECCV 2022.

## At a glance

![LeRF-CVPR-Github](https://raw.githubusercontent.com/ddlee-cn/LeRF-PyTorch/main/docs/LeRF-At-A-Glance.png)

Thanks to the assumption in resampling function, e.g., cubic, interpolation rocks in terms of efficiency and generalization capacity.

LeRF (Learning Resampling Function) achieves adaptive interpolation via assigning steerable resampling functions to image pixels and learning them in a data-driven way.

LeRF runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly.

Please learn more at our [project page](https://lerf.pages.dev).

Related work: [MuLUT](https://mulut.pages.dev), [DFC](https://openaccess.thecvf.com/content/CVPR2024/html/Li_Look-Up_Table_Compression_for_Efficient_Image_Restoration_CVPR_2024_paper.html)

## Usage

### Dataset

The paths to datasets are configured in `common/option.py`.

#### Arbitrary-Scale Super-Resolution

Please download the SR benchmark datasets following the instruction of [BasicSR](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md#common-image-sr-datasets).

Then, put the downloaded SR benchmark datasets here as the following structure. `[testset]` can be `['Set5', 'Set14', 'B100', 'Urban100', 'Manga109', 'div2k']`.

```bash
datasets/rrBenchmark/
                   /[testset]/HR/*.png
                             /LR_bicubic/X2/*.png
                             /LR_bicubic/X1.50_2.00/*.png
                   /...
```

LR images with non-integer downscale factors can be obtained with the `imresize` function in MATLAB or [ResizeRight](https://github.com/assafshocher/ResizeRight).

#### Homographic Warping

Please download the warping benchmark datasets following the instruction of [LTEW](https://github.com/jaewon-lee-b/ltew).

```bash
datasets/WarpBenchmark/
                   /[testset]/HR/*.png
                             /isc/*.png
                             /isc/*.pth
                             /osc/*.png
                             /osc/*.pth
                   /...
```

### Code overview

```bash
└──lerf
  ├── README.md
  ├── common
    ├── network.py                         # Basic blocks
    ├── option.py                          # Options
    └── utils.py                           # Utility functions
  ├── resample
    ├── data.py                            # Training and validation data loader
    ├── model.py                           # LeRF model definition
    ├── train_model.py                     # LeRF model training and fine-tuning script
    ├── transfer_to_lut.py                 # Script for tranfering hyper-parameter learning network to LUTs
    ├── eval_model.py                      # Evaluation of LeRF-Net and LeRF-Net++
    ├── eval_lut_sr.py                     # Evaluation of LeRF-L and LeRF-G on SR
    └── eval_lut_warp.py                   # Evaluation of LeRF-L and LeRF-G on Warping
  ├── resize_right
    ├── interp_methods.py                  # Fixed resampling function
    ├── resize_right.py                    # Interpolation methods implementation
    ├── resize_right2d_numpy.py            # LeRF core implementation in numpy
    └── resize_right2d_torch.py            # LeRF core implementation in pytorch
  ├── models
    ├── lerf-l
      ├── LUTft_*.npy                      # LeRF-L LUTs
      ├── Model_050000.pth                 # LeRF-L Model checkpoint
    ├── lerf-g
      ├── LUTft_*.npy                      # LeRF-G LUTs (called lerf-lut in the MindSpore repo)
      ├── Model_050000.pth                 # LeRF-G Model checkpoint (called lerf-net in the MindSpore repo, slightly different after retraining)
    ├── lerf-net
      ├── Model_050000.pth                 # LeRF-Net Model checkpoint
    ├── lerf-net-plus
      ├── Model_050000.pth                 # LeRF-Net++ Model checkpoint
  ├── scripts.sh                           # Traning and Evaluation script
  └── requirements.txt                     # Dependencies
```

### Step 0: Installation

Clone this repo.

```
git clone https://github.com/ddlee-cn/LeRF-PyTorch
```

Install requirements: `torch>=1.5.0`, `opencv-python`, `scipy`

In the following, we take the LeRF-G model (conference version) as an example. Please refer to `scripts.sh` for other configurations as well as reference outputs for your reproduction.

### Step 1: Training LeRF model

```
python resample/train_model.py -e models/lerf-g/ --twoStage --outC 3
```

### Step 2: Transfer networks into LUTs

```
python resample/transfer_to_lut.py -e models/lerf-g/ --outC 3
```

### Step 3: Fine-tuning LUTs

```
python resample/train_model.py -e models/lerf-g/ --lutft --model SWF2LUT --twoStage --outC 3 --batchSize 256
```

After fine-tuning, LUTs are saved into `.npy` files and can be deployed on other devices.

### Step 4: Evaluation of LUTs

```
python resample/eval_lut_sr.py --testDir data/rrBenchmark --resultRoot results/sr --lutName LUTft -e models/lerf-g
```

## Contact
If you have any questions, feel free to contact me by e-mail `jclee [at] mail.ustc.edu.cn`.

## Citation
If you find our work helpful, please cite the following papers.

```
@InProceedings{Li_2022_MuLUT,
    author    = {Li, Jiacheng and Chen, Chang and Cheng, Zhen and Xiong, Zhiwei},
    title     = {{MuLUT}: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution},
    booktitle = {ECCV},
    year      = {2022},
  }

@ARTICLE{10530442,
  author={Li, Jiacheng and Chen, Chang and Cheng, Zhen and Xiong, Zhiwei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Toward DNN of LUTs: Learning Efficient Image Restoration With Multiple Look-Up Tables}, 
  year={2024},
  volume={46},
  number={12},
  pages={8284-8301},
  doi={10.1109/TPAMI.2024.3401048}}

@InProceedings{Li_2023_LeRF,
      author    = {Li, Jiacheng and Chen, Chang and Huang, Wei and Lang, Zhiqiang and Song, Fenglong and Yan, Youliang and Xiong, Zhiwei},
      title     = {Learning Steerable Function for Efficient Image Resampling},
      booktitle = {CVPR},
      year      = {2023},
  }

@ARTICLE{11027639,
  author={Li, Jiacheng and Chen, Chang and Song, Fenglong and Yan, Youliang and Xiong, Zhiwei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation}, 
  year={2025},
  volume={},
  number={},
  pages={1-18},
  doi={10.1109/TPAMI.2025.3577227}}
```

## License
MIT

## Acknoledgement

The resize part of our code is modified based on [ResizeRight](https://github.com/assafshocher/ResizeRight).

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