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..
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
Authors: Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Xiaotian Han, Zhengyu Chen, Quanzeng You, Hongxia Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our exhaustive experiments confirm Dream Clear s superior performance, underlining the efficacy of our dual strategy for real-world image restoration. |
| Researcher Affiliation | Collaboration | MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences Byte Dance, Inc University of Science and Technology of China |
| Pseudocode | No | The paper describes methods through text and diagrams (e.g., Figure 2, Figure 3) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models: https://github.com/shallowdream204/Dream Clear |
| Open Datasets | Yes | We adopt a combination of DIV2K [44], Flickr2K [2], LSDIR [39], DIV8K [22], and our generated dataset to train Dream Clear. |
| Dataset Splits | Yes | For synthetic benchmarks, we randomly crop 3,000 patches from the validation sets of DIV2K and LSDIR, and degrade them using the same settings as training. |
| Hardware Specification | Yes | The proposed Gen IR framework is built on SDXL [55] and trained over 5 days using 16 NVIDIA A100 GPUs. ... The training is conducted on 1024 1024 resolution images, running for 7 days on 32 NVIDIA A100 GPUs with a batch-size of 128. |
| Software Dependencies | No | The paper mentions specific models and optimizers (e.g., SDXL, Pix Art-α, LLa VA, AdamW optimizer) but does not provide specific version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The training is conducted on 1024 1024 resolution images, running for 7 days on 32 NVIDIA A100 GPUs with a batch-size of 128. The number of experts K in Eq. (2) is set to 3. For inference of Dream Clear, we adopt iDDPM [51] with 50 sampling steps, CFG guidance scale ω = 4.5. |