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..
Spatial-Spectral Transformer for Hyperspectral Image Denoising
Authors: Miaoyu Li, Ying Fu, Yulun Zhang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results. |
| Researcher Affiliation | Academia | Miaoyu Li1, Ying Fu1*, Yulun Zhang2 1Beijing Institute of Technology 2ETH Z urich |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | The code is released at https://github.com/Myu Li/SST. |
| Open Datasets | Yes | We evaluate our method mainly on ICVL (Arad and Ben-Shahar 2016) dataset. |
| Dataset Splits | No | The paper only specifies a training and testing split (100 HSIs for training and 50 HSIs for testing) but does not explicitly mention a separate validation set split or cross-validation details for reproducibility. |
| Hardware Specification | Yes | Competing deep learning methods (HSID-CNN, QRNN3D, and T3SC) and our proposed Transformer are implemented with Py Torch and run with a Ge Force RTX 3090. Traditional methods, including BM4D, LLRT, TSLRLN, and NG-Meet, are implemented with Matlab and run with an Intel Core i910850K CPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Matlab' as implementation environments but does not provide specific version numbers for these or any other software libraries or dependencies. |
| Experiment Setup | Yes | We use Adam (Kingma and Ba 2014) to optimize the network with parameters initialized by Xavier initialization (Glorot and Bengio 2010). The batch size is set to 8 with 100 epochs of training. The learning rate is set to 1 10 4 and is divided by 10 after 60 epoch. |