https://github.com/lingyzhu0101/bit_depth_expansion
[MMSP'24] Diffusion-based Bit-depth Expansion
https://github.com/lingyzhu0101/bit_depth_expansion
computer-vision deep-learning diffusion-models generative-model
Last synced: 4 months ago
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[MMSP'24] Diffusion-based Bit-depth Expansion
- Host: GitHub
- URL: https://github.com/lingyzhu0101/bit_depth_expansion
- Owner: lingyzhu0101
- Created: 2024-11-14T07:02:29.000Z (over 1 year ago)
- Default Branch: lingyzhu0101
- Last Pushed: 2024-11-15T03:14:11.000Z (over 1 year ago)
- Last Synced: 2025-12-29T18:17:08.421Z (6 months ago)
- Topics: computer-vision, deep-learning, diffusion-models, generative-model
- Homepage:
- Size: 6.09 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# [MMSP'24] Bit_Depth_Expansion (Diffusion)
Official Pytorch implementation of **Diffusion-based Bit-depth Expansion**.
[Riyu Lu](),
[Lingyu Zhu](https://scholar.google.com/citations?user=IhyTEDkAAAAJ&hl=zh-CN),
[Baoliang Chen](https://scholar.google.com/citations?user=w_WL27oAAAAJ&hl=zh-CN),
[Xiaopeng Fan](https://scholar.google.com/citations?user=4LsZhDgAAAAJ&hl=zh-CN),
[Shiqi Wang](https://scholar.google.com/citations?user=Pr7s2VUAAAAJ&hl=zh-CN)
[[`PDF`](https://ieeexplore.ieee.org/document/10743597)] [[`Poster`]()] [[`Presentation`](src/figures/Presentation.pdf)]
## Overview
Diffusion-based generative models have achieved remarkable success across a variety of applications. However, the potential application for bit-depth expansion has not been extensively studied. This paper introduces a wavelet-based diffusion model for the bit-depth expansion task. In this method, the image is first decomposed into low and high-frequency components via wavelet transformation. This decomposition allows for targeted processing by specialized modules and reduces computational complexity by lowering the image resolution. The low-frequency component is processed in both the forward diffusion and reverse denoising stages. Meanwhile, the high-frequency components are
filtered by the High Frequency Denoising Filter (HFDF) to eliminate noise and artifacts. Finally, the low and high-frequency components are recombined into a predicted high-bit-depth image through inverse wavelet transformation. Experimental results demonstrate the superiority of the proposed method in producing perceptually compelling outputs that outperform previous methods.
## Qualitative Performance
## TODO List
This repository is still under active construction:
- [ ] Release training and testing codes
- [ ] Release pretrained models
- [ ] Clean the code
## Contact
- Lingyu Zhu: lingyzhu-c@my.cityu.edu.hk
- Riyu Lu: riyulu2-c@my.cityu.edu.hk
## Citation
If you find our work helpful, please consider citing:
```bibtex
@INPROCEEDINGS{10743597,
author={Lu, Riyu and Zhu, Lingyu and Chen, Baoliang and Fan, Xiaopeng and Wang, Shiqi},
booktitle={2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)},
title={Diffusion-Based Bit-Depth Expansion},
year={2024}}
```