Universal Image Restoration Pre-training via Degradation Classification Article Swipe
JiaKui Hu
,
Lujia Jin
,
Zhengjian Yao
,
Yanye Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15510
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15510
This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image restoration pre-training. Unlike the existing self-supervised pre-training methods, DCPT utilizes the degradation type of the input image as an extremely weak supervision, which can be effortlessly obtained, even intrinsic in all image restoration datasets. DCPT comprises two primary stages. Initially, image features are extracted from the encoder. Subsequently, a lightweight decoder, such as ResNet18, is leveraged to classify the degradation type of the input image solely based on the features extracted in the first stage, without utilizing the input image. The encoder is pre-trained with a straightforward yet potent DCPT, which is used to address universal image restoration and achieve outstanding performance. Following DCPT, both convolutional neural networks (CNNs) and transformers demonstrate performance improvements, with gains of up to 2.55 dB in the 10D all-in-one restoration task and 6.53 dB in the mixed degradation scenarios. Moreover, previous self-supervised pretraining methods, such as masked image modeling, discard the decoder after pre-training, while our DCPT utilizes the pre-trained parameters more effectively. This superiority arises from the degradation classifier acquired during DCPT, which facilitates transfer learning between models of identical architecture trained on diverse degradation types. Source code and models are available at https://github.com/MILab-PKU/dcpt.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.15510
- https://arxiv.org/pdf/2501.15510
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406880675
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406880675Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.15510Digital Object Identifier
- Title
-
Universal Image Restoration Pre-training via Degradation ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-26Full publication date if available
- Authors
-
JiaKui Hu, Lujia Jin, Zhengjian Yao, Yanye LuList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.15510Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2501.15510Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2501.15510Direct OA link when available
- Concepts
-
Degradation (telecommunications), Image restoration, Training (meteorology), Image (mathematics), Computer science, Artificial intelligence, Pattern recognition (psychology), Computer vision, Image processing, Geography, Telecommunications, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.improvements, | 138 |
| abstract_inverted_index.pre-training, | 174 |
| abstract_inverted_index.pre-training. | 26 |
| abstract_inverted_index.Classification | 5 |
| abstract_inverted_index.self-supervised | 30, 162 |
| abstract_inverted_index.straightforward | 111 |
| abstract_inverted_index.https://github.com/MILab-PKU/dcpt. | 215 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |