Hyperspectral Image Denoising Using Uncertainty-Aware Adjustor
Authors: Jiahua Xiao, Xing Wei
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results validate that the proposed solution can improve over recent state-of-the-art (SOTA) methods on both simulated and real-world benchmarks by a large margin. |
| Researcher Affiliation | Academia | School of Software Engineering, Xi an Jiaotong University xjh847286495@stu.xjtu.edu.cn, weixing@mail.xjtu.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The training process is performed on the CAVE dataset [Park et al., 2007], and we evaluate these models on the ICVL dataset [Arad and Ben-Shahar, 2016] and the remotely sensed dataset WDCM1 for simulation experiments. For real-data experiments, we use the real-world datasets Urban [Mnih and Hinton, 2010] and Indian Pines [Landgrebe, 2003]. |
| Dataset Splits | No | The paper mentions training on CAVE and evaluating on ICVL/WDCM/Urban/Indian Pines, but does not provide specific details on training/validation/test dataset splits (e.g., percentages, sample counts, or explicit validation set definition). |
| Hardware Specification | Yes | All experiments are conducted on the NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | Following common protocols in existing spectral auxiliary networks, the number K of adjacent bands is set to 24. The number N of the short local range is set to 4. The working mechanism of UA-Adjustor is to be crafted on spectral auxiliary networks and these two parts are trained as a whole architecture. In practice, the complex noises randomly selected from Case 1 to Case 3 are added to the training set. |