1 University of Würzburg, Germany - 2 Shanghai Jiao Tong University, China - 3 ETH Zürich, Switzerland
05/29/2024: Added 🤗Demo.05/23/2024: Code & ckpt & results release. Google Drive05/02/2024: SeemoRe has been accepted at ICML 2024! 🎉02/05/2024: Technical report released on arxiv.
Abstract
Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings.
Mixture of Low Rank Experts:
Main Results
Visual Comparison
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Create a conda enviroment:
ENV_NAME="seemore"
conda create -n $ENV_NAME python=3.10
conda activate $ENV_NAME
Run following script to install the dependencies:
bash install.sh
Pre-trained checkpoints and visual results can be downloaded here. Place the checkpoints in checkpoints/.
In options you can find the corresponding config files for reproducing our experiments.
For testing the pre-trained checkpoints please use following commands. Replace [TEST OPT YML] with the path to the corresponding option file.
python basicsr/test.py -opt [TEST OPT YML]
For single-GPU training use the following commands. Replace [TRAIN OPT YML] with the path to the corresponding option file.
torchrun --nproc_per_node=1 --master_port=4321 basicsr/train.py -opt [TRAIN OPT YML] --launcher pytorch
If you find our work helpful, please consider citing the following paper and/or ⭐ the repo.
@inproceedings{zamfir2024details,
title={See More Details: Efficient Image Super-Resolution by Experts Mining},
author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte},
booktitle={International Conference on Machine Learning},
year={2024},
organization={PMLR}
}
This code is built on BasicSR.
Copyright (c) 2025 Computer Vision Lab, University of Wurzburg Licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International) (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
The code is released for academic research use only. For commercial use, please contact Computer Vision Lab, University of Wurzburg. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
