Stochastic Window Transformer for Image Restoration
Authors: Jie Xiao, Xueyang Fu, Feng Wu, Zheng-Jun Zha
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments validate the stochastic window strategy consistently improves performance on various image restoration tasks (deraining, denoising and deblurring) by significant margins. |
| Researcher Affiliation | Academia | Jie Xiao, Xueyang Fu , Feng Wu, Zheng-Jun Zha University of Science and Technology of China, Hefei, China ustchbxj@mail.ustc.edu.cn, {xyfu,fengwu,zhazj}@ustc.edu.cn |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual explanations (e.g., Equations (1), (2), (4), (5)) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code is available at https://github.com/jiexiaou/Stoformer. |
| Open Datasets | Yes | All these methods are evaluated on SPA-Data [51]... We conduct denoising experiments on the additive white Gaussian Noise benchmark datasets, which include Set12 [66], BSD68 [37], Urban100 [19], Kodak24 [12] and Mc Master [71]... We also perform deblurring experiments on the benchmark datasets (Go Pro [39] and HIDE [45]). |
| Dataset Splits | No | The training samples are augmented by the horizontal flipping and rotation of 90 , 180 , or 270 . Please refer to the supplemental material for task-specific settings. The paper states 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See supplemental material.' The main text does not explicitly detail the train/validation/test splits, only referencing the supplemental material. |
| Hardware Specification | No | The paper states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplemental material.' indicating that specific hardware details are not provided in the main text. |
| Software Dependencies | No | The paper mentions using 'Charbonnier loss' and 'Adam optimizer' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Loss Function. The loss function adopted for training is the Charbonnier loss [1], whose mathematical expression is: L(I , I) = p ||I I||2 + ϵ2, (6) where I and I are the restored and ground-truth image respectively. The constant ϵ is empirically set to 10 3. Training Detail. Stoformer employs a four-level encoder-decoder structure. The numbers of Sto Block are {1, 2, 8, 8} for level-1 to level-4 of Encoder and the blocks for Decoder are mirrored. The number of channel is set to 32 and the window size is 8 8. We train the network with Adam optimizer (β1 = 0.9, β2 = 0.999) with the initial learning rate 3 10 4 gradually reduced to 1 10 6 with the cosine annealing. The training samples are augmented by the horizontal flipping and rotation of 90 , 180 , or 270 . Please refer to the supplemental material for task-specific settings. |