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 | Conference PDF | Archive PDF | Plain Text | 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. |