WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows
Authors: Chunxiao Li, Xuejing Kang, Anlong Ming
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera s RGB dataset. |
| Researcher Affiliation | Academia | Chunxiao Li , Xuejing Kang , Anlong Ming School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications {chunxiaol, kangxuejing, mal}@bupt.edu.cn |
| Pseudocode | No | The paper describes the model architecture and processes but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ChunxiaoLe/WBFlow. |
| Open Datasets | Yes | Following [Afifi and Brown, 2020a], we randomly selected 12000 s RGB images from the first fold of Set1 [Afifi et al., 2019a; Cheng et al., 2014] to train WBFlow. |
| Dataset Splits | No | The paper mentions training and testing on specific datasets (Set1, Set1-Test, Set2, Rendered Cube Dataset) but does not explicitly describe a separate validation set or detailed train/validation/test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | We implemented WBFlow on Pytorch with CUDA support and used the Adam [Kingma and Ba, 2014] with β1 = 0.9 and learning rate 10 4 to optimize it. |
| Experiment Setup | Yes | For the experiments with all training images, we trained WBFlow for 340000 iterations with batch size 4. While for few-shot experiments, we trained CT for 15000 iterations. We used the Adam [Kingma and Ba, 2014] with β1 = 0.9 and learning rate 10 4 to optimize it. We used color jittering, average blur, geometric rotation, and flipping to augment data. During testing, following [Afifi and Brown, 2020a], we resized all input images to a maximum dimension of 656 pixels and set a color mapping procedure to compute the final white-balanced s RGB images. |