Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Universal Image Restoration Pre-training via Degradation Classification
Authors: Jiakui Hu, Lujia Jin, Zhengjian Yao, Yanye Lu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our DCPT in four different settings. (1) All-in-One: A single model is fine-tuned after DCPT to perform image restoration across multiple degradation. Following previous state-of-the-art works (Zhang et al., 2023; Luo et al., 2023b), we evaluate on five restoration (5D) tasks and 10D tasks. (2) Single-task: We report the performance on unseen degradation of all-in-one trained models without fine-tuning following (Zhang et al., 2023; Ai et al., 2024).To highlight DCPT s impact on single-task pre-training, we present fine-tuning results of DCPT pre-trained models in specific singletask settings. (3) Mixed degradation: We evaluate the fine-tuned model under mixed degradation to verify whether DCPT is suitable for the restoration of complex mixed-degraded images, such as composite weather. (4) Transfer learning: We evaluate the transfer learning capability of restoration models trained by DC-guided training or not between different image restoration tasks. |
| Researcher Affiliation | Collaboration | 1Institute of Medical Technology, Peking University Health Science Center, Peking University 2Biomedical Engineering Department, College of Future Technology, Peking University 3National Biomedical Imaging Center, Peking University 4China Mobile Research Institute |
| Pseudocode | Yes | To illustrate the simplicity and efficacy of DCPT, we present the Py Torch-like code of DCPT here. We hope that this code will further improve the reproducibility of DCPT. ### train to generate the clean image encoder.train() decoder.eval() optimizer_encoder.zero_grad() pix_output = encoder(gt, hook=False) l_total = 0 # pixel loss if cri_pixel: l_pix = cri_pixel(pix_output, gt) l_total += l_pix ### train to classify the degradation decoder.train() optimizer_decoder.zero_grad() hook_outputs = encoder(lq, hook=True) cls_output = decoder(lq, hook_outputs[::-1]) # classification loss if cri_cls: l_cls = cri_cls(cls_output, dataset_idx) l_total += l_cls l_total.backward() optimizer_encoder.step() optimizer_decoder.step() |
| Open Source Code | Yes | Source code and models are available at https://github.com/MILab-PKU/dcpt. |
| Open Datasets | Yes | For 3D and 5D all-in-one restoration, following Air Net (Li et al., 2022), a combination of various image restoration datasets is employed: Rain200L (Yang et al., 2017), which contains 200 training images for deraining; RESIDE (Li et al., 2018), which contains 72,135 training images and 500 test images (SOTS) for dehazing; BSD400 (Martin et al., 2001b) and WED (Ma et al., 2016), which contain 5,144 training images for Gaussian denoising; Go Pro (Nah et al., 2017), which contains 2,103 training images and 1,111 test images for single image motion deblurring; and LOL (Wei et al., 2018), which contains 485 training images and 15 test images for low-light enhancement. |
| Dataset Splits | Yes | Before the experiment, we randomly divide the training set and the test set in a ratio of 2:1. We ensure that the data volume of each degradation in the training set and the test set is evenly distributed. |
| Hardware Specification | Yes | During DCPT, image restoration models (encoder) and degradation classifiers (decoder) are all trained by Adam W (Kingma & Ba, 2014) with no weight decay for 100k iters with batch-size 32 on 128 128 image patches on 4 NVIDIA L40 GPUs. |
| Software Dependencies | No | The paper implies the use of PyTorch through "PYTORCH-LIKE CODE", and mentions "Adam W (Kingma & Ba, 2014)" as an optimizer. However, it does not provide specific version numbers for these software components or any other libraries used. |
| Experiment Setup | Yes | During DCPT, image restoration models (encoder) and degradation classifiers (decoder) are all trained by Adam W (Kingma & Ba, 2014) with no weight decay for 100k iters with batch-size 32 on 128 128 image patches on 4 NVIDIA L40 GPUs. Due to the heterogeneous encoder-decoder design, we employ distinct learning rates for encoder and decoder. The learning rate is set to 3 10 4 for encoder and 1 10 4 for decoder. The learning rate does not alter during the DCPT. |