Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Low-Contrast Image Enhancement using Structure Tensor Representation
Authors: Hyungjoo Jung, Hyunsung Jang, Namkoo Ha, Kwanghoon Sohn1725-1733
AAAI 2021 | Venue PDF | 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. |