A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Authors: Theo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on various denoising benchmarks that our method is computationally efficient and significantly outperforms the state of the art.We experimentally evaluate our HSI model on standard denoising benchmarks, showing a significant improvement over the state of the art (including deep learning models and more traditional baselines), while being computationally very efficient at test time. |
| Researcher Affiliation | Academia | Théo Bodrito , Alexandre Zouaoui , Jocelyn Chanussot, and Julien Mairal Inria, Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France firstname.lastname@inria.fr |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/inria-thoth/T3SC. |
| Open Datasets | Yes | We evaluate our approach on two datasets with significantly different properties. ICVL [4] consists of 204 images of size 1392 1300 with 31 bands. ... Washington DC Mall is perhaps the most widely used dataset2 for HSI denoising and consists of a high-quality image of size 1280 307 with 191 bands. ... Specific experiments were also conducted with the datasets APEX [28], Pavia3, Urban[58] and CAVE [64], which appear in the supplementary material. |
| Dataset Splits | No | We used 100 images for training and 50 for testing as in [62] but with a different train/test split ensuring that similar images e.g., picture from the same scene are not used twice. ... Following [54], we split the image into two sub-images of size 600 307 and 480 307 for training and one sub-image of size 200 200 for testing. The paper does not explicitly state a validation split. |
| Hardware Specification | Yes | SMDS, QRNN3D and T3SC are using a V100 GPU; BM4D, GLF, LLRT and NGMeet are using an Intel(R) Xeon(R) CPU E5-1630 v4 @ 3.70GHz. |
| Software Dependencies | No | The paper mentions 'standard deep learning frameworks' but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We trained our network by minimizing the MSE between the groundtruth and restored images. For ICVL, we follow the training procedure described in [62]: we first center crop training images to size 1024 1024, then we extract patches of size 64 64 at scales 1:1, 1:2, and 1:4, with stride 64, 32 and 32 respectively. ... Basic data augmentation schemes such as 90 rotations and vertical/horizontal flipping are performed. ... the number of unrolled iterations chosen for the first and second layers are 12 and 5 respectively. |