Adaptive Domain Learning for Cross-domain Image Denoising
Authors: Zian Qian, Chenyang Qi, Ka Law, Hao Fu, Chenyang Lei, Qifeng Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor. |
| Researcher Affiliation | Collaboration | Zian Qian1 Chenyang Qi1 Ka Lung Law2 Hao Fu2 Chenyang Lei3 Qifeng Chen1 1HKUST 2Sense Time 3CAIR, HKISI-CAS |
| Pseudocode | Yes | Algorithm 1 Adaptive domain learning (ADL) |
| Open Source Code | No | Justification: We will release the code in the future. |
| Open Datasets | Yes | We conduct extensive experiments on public datasets with various smartphone and DSLR cameras... SIDD [2] is a popular RAW denoising dataset... ELD Dataset [42] contains RAW data... SID dataset [5] captured RAW data... |
| Dataset Splits | Yes | At the beginning of the training, k is set to 20% of the size of T adp and increases during the training process. At the end of the training, 50% of T adp will be used. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) are provided for the experimental setup. |
| Software Dependencies | No | The paper mentions 'Adam W as the optimizer' but does not provide version numbers for any software dependencies, libraries, or programming languages. |
| Experiment Setup | Yes | We train our ADL in 300k iterations, using Adam W as the optimizer with a learning rate of 3 10 3. |