Polarization-Aware Low-Light Image Enhancement
Authors: Chu Zhou, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, Boxin Shi
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform simulation on 6000 synthetic scenes to quantitatively obtain the relationships between the average error rates (EIavg, ES0,1,2, Ep, Eθ) and γ, as show in Fig. 1 (c)4. We compare our method to IPLNet6 (Hu et al. 2020) (the only existing method designed for enhancing polarized lowlight images as far as we know), and three state-of-the-art single-image low-light enhancement methods including Enlighten GAN (Jiang et al. 2021), UTVNet (Zheng, Shi, and Shi 2021), and Zero-DCE (Guo et al. 2020) on the PLIE dataset. We conduct a series of ablation studies and show comparisons in Tab. 2. |
| Researcher Affiliation | Academia | 1Key Laboratory of Machine Perception (MOE), School of Intelligence Science and Technology, Peking University 2National Engineering Research Center of Visual Technology, School of Computer Science, Peking University 3School of Artificial Intelligence, Beijing University of Posts and Telecommunications |
| Pseudocode | No | The paper describes the network architecture and process flow with a diagram (Fig. 2) and text, but it does not provide formal pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide a direct link to the source code for its proposed method or explicitly state that the code is publicly released. |
| Open Datasets | No | The paper states: 'Therefore, we propose to build a real-world dataset, named PLIE (Polarization-aware Low-light Image Enhancement) dataset5, which contains pairwise low- and normal-light polarized images to train our network and test it quantitatively and qualitatively.' Footnote 5 states 'More information can be found in the supplementary material.' However, no concrete access information (e.g., URL, DOI, or formal citation with author/year for the dataset itself) is provided in the main text to confirm public availability. |
| Dataset Splits | No | The paper states that they train and test on the PLIE dataset but does not provide specific split percentages or sample counts for training, validation, or test sets. |
| Hardware Specification | Yes | We implement the network using Py Torch on an NVIDIA 2080Ti GPU |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'ADAM optimizer' but does not specify their version numbers. |
| Experiment Setup | Yes | We implement the network using Py Torch on an NVIDIA 2080Ti GPU, and train it for 400 epochs using ADAM optimizer (Kingma and Ba 2014) with a batch size of 8. The learning rate is set to 0.01. |