Universal Image Restoration Pre-training via Masked Degradation
  Classification
This study introduces a Masked Degradation Classification Pre-Training method
(MaskDCPT), designed to facilitate the classification of degradation types in
input images, leading to comprehensive image restoration pre-training. Unlike
conventional pre-training methods, MaskDCPT uses the degradation type of the
image as an extremely weak supervision, while simultaneously leveraging the
image reconstruction to enhance performance and robustness. MaskDCPT includes
an encoder and two decoders: the encoder extracts features from the masked
low-quality input image. The classification decoder uses these features to
identify the degradation type, whereas the reconstruction decoder aims to
reconstruct a corresponding high-quality image. This design allows the
pre-training to benefit from both masked image modeling and contrastive
learning, resulting in a generalized representation suited for restoration
tasks. Benefit from the straightforward yet potent MaskDCPT, the pre-trained
encoder can be used to address universal image restoration and achieve
outstanding performance. Implementing MaskDCPT significantly improves
performance for both convolution neural networks (CNNs) and Transformers, with
a minimum increase in PSNR of 3.77 dB in the 5D all-in-one restoration task and
a 34.8% reduction in PIQE compared to baseline in real-world degradation
scenarios. It also emergences strong generalization to previously unseen
degradation types and levels. In addition, we curate and release the UIR-2.5M
dataset, which includes 2.5 million paired restoration samples across 19
degradation types and over 200 degradation levels, incorporating both synthetic
and real-world data. The dataset, source code, and models are available at
https://github.com/MILab-PKU/MaskDCPT.
68f0754736f8b025381e19f9
JiaKui Hu
claimed_verified
2025-10-16T11:40:34.324Z
64ccd5cc4726a3f833831087
/avatars/6364b1ebb1d06c68245f4c786fb07ee9.svg
Hu
user
Jiakui
68f0754736f8b025381e19fa
Zhengjian Yao
68f0754736f8b025381e19fb
Lujia Jin
claimed_verified
2025-10-16T10:38:28.848Z
68776bf57ef0f82c9b71e166
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/GYOfXhxWN0Xg1hyGOKDpA.png
Lujia Jin
user
jinlujia
68f0754736f8b025381e19fc
Yinghao Chen
68f0754736f8b025381e19fd
Yanye Lu
Universal Image Restoration Pre-training via Masked Degradation
  Classification
This study introduces a Masked Degradation Classification Pre-Training method
(MaskDCPT), designed to facilitate the classification of degradation types in
input images, leading to comprehensive image restoration pre-training. Unlike
conventional pre-training methods, MaskDCPT uses the degradation type of the
image as an extremely weak supervision, while simultaneously leveraging the
image reconstruction to enhance performance and robustness. MaskDCPT includes
an encoder and two decoders: the encoder extracts features from the masked
low-quality input image. The classification decoder uses these features to
identify the degradation type, whereas the reconstruction decoder aims to
reconstruct a corresponding high-quality image. This design allows the
pre-training to benefit from both masked image modeling and contrastive
learning, resulting in a generalized representation suited for restoration
tasks. Benefit from the straightforward yet potent MaskDCPT, the pre-trained
encoder can be used to address universal image restoration and achieve
outstanding performance. Implementing MaskDCPT significantly improves
performance for both convolution neural networks (CNNs) and Transformers, with
a minimum increase in PSNR of 3.77 dB in the 5D all-in-one restoration task and
a 34.8% reduction in PIQE compared to baseline in real-world degradation
scenarios. It also emergences strong generalization to previously unseen
degradation types and levels. In addition, we curate and release the UIR-2.5M
dataset, which includes 2.5 million paired restoration samples across 19
degradation types and over 200 degradation levels, incorporating both synthetic
and real-world data. The dataset, source code, and models are available at
https://github.com/MILab-PKU/MaskDCPT.
A Masked Degradation Classification Pre-Training method enhances image restoration by using degradation type classification and image reconstruction, improving performance across CNNs and Transformers.
Masked Degradation Classification Pre-Training MaskDCPT encoder decoders masked image modeling contrastive learning PSNR PIQE UIR-2.5M dataset