Deep Low-Contrast Image Enhancement using Structure Tensor Representation
Authors: Hyungjoo Jung, Hyunsung Jang, Namkoo Ha, Kwanghoon Sohn1725-1733
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide indepth analysis on our method and comparison with conventional loss functions. Quantitative and qualitative evaluations demonstrate that the proposed method outperforms the existing state-of-the art approaches in various benchmarks. |
| Researcher Affiliation | Collaboration | 1 Yonsei University 2 Korea Institute of Science and Technology (KIST) 3 LIG Nex1 |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Tensor Flow library with 12GB NIVIDIA Titan GPU is used for network construction and training (our code will be made publicly available). |
| Open Datasets | Yes | Our enhancement method requires the multi-exposure image sequences to train the network DCNN. Recently, large-scale multi-exposure image dataset (Cai, Gu, and Zhang 2018) has been constructed including both indoor and outdoor scenes. We trained our network using 7 multi-exposure sequences for each image from (Cai, Gu, and Zhang 2018), which covers most of the exposure levels. |
| Dataset Splits | No | The paper states: "We randomly cropped 5 × 104 patches with 128 × 128 size from our training dataset, and trained our network using the patches." It does not specify distinct training, validation, or test splits with percentages or counts for reproduction. |
| Hardware Specification | Yes | The Tensor Flow library with 12GB NIVIDIA Titan GPU is used for network construction and training. |
| Software Dependencies | No | The paper mentions "The Tensor Flow library" but does not specify a version number. |
| Experiment Setup | Yes | The loss function of (4) is minimized with the Adam solver (Kingma and Ba 2014) (β1 = 0.9, β2 = 0.999, and ϵ = 10 −8 setting). We randomly cropped 5 × 104 patches with 128 × 128 size from our training dataset, and trained our network using the patches. The learning rate was initialized as 10 −3 and halved every 10 epoches until 100 epoches. |