Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement
Authors: Dehuan Zhang, Jingchun Zhou, Chunle Guo, Weishi Zhang, Chongyi Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Datasets, Implementation Details, Comparison Results, Ablation Study |
| Researcher Affiliation | Academia | 1 College of Information Science and Technology, Dalian Maritime University 2 VCIP, CS, Nankai University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at: https: //github.com/zhoujingchun03/SMDR-IS |
| Open Datasets | Yes | We trained SMDR-IS utilizing the UIEB dataset, which consists of 800 training images and 90 paired testing images. To assess the robustness of SMDR-IS, we further conducted the evaluation on various datasets, i.e. UIEB, U45, LSUI. Li et al. 2019. An Underwater Image Enhancement Benchmark Dataset and Beyond. IEEE Transactions on Image Processing, 29: 4376 4389. |
| Dataset Splits | No | The paper mentions 800 training images and 90 paired testing images for the UIEB dataset but does not explicitly detail a validation split or its size. |
| Hardware Specification | Yes | Our method was implemented using Py Torch, with an NVIDIA Tesla V100 GPU, Intel(R) Xeon(R) Silver 4114 CPU, and 32GB RAM. |
| Software Dependencies | No | The paper mentions "PyTorch" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For training, images were randomly cropped to a resolution of 256 x 256, and we used a batch size of 44 and a learning rate of 0.0002. |