Omni-Kernel Network for Image Restoration
Authors: Yuning Cui, Wenqi Ren, Alois Knoll
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our network achieves state-of-the-art performance on 11 benchmark datasets for three representative image restoration tasks, including image dehazing, image desnowing, and image defocus deblurring. |
| Researcher Affiliation | Academia | Yuning Cui1, Wenqi Ren2*, Alois Knoll1 1Technical University of Munich 2Shenzhen Campus of Sun Yat-sen University |
| Pseudocode | No | The paper describes the architecture and components in text and diagrams (Figure 2) but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/c-yn/OKNet. |
| Open Datasets | Yes | We conduct dehazing experiments on three kinds of datasets: daytime synthetic dataset (RESIDE (Li et al. 2018)), daytime real-world datasets (Dense-Haze (Ancuti et al. 2019), NH-HAZE (Ancuti, Ancuti, and Timofte 2020), O-Haze (Ancuti et al. 2018b), and I-Haze (Ancuti et al. 2018a)), and nighttime dataset (NHR (Zhang et al. 2020))... verify the effectiveness of the proposed network for single-image defocus deblurring using the widely used DPDD (Abuolaim and Brown 2020) dataset... Snow100K (Liu et al. 2018), SRRS (Chen et al. 2020), and CSD (Chen et al. 2021b). |
| Dataset Splits | No | The paper mentions using benchmark datasets for training and evaluation (e.g., "training OKNet-S on the RESIDE-Indoor (Li et al. 2018) dataset for 300 epochs and evaluating on SOTS-Indoor (Li et al. 2018)"), but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) for these datasets within the paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the "Adam optimizer" and "cosine annealing decay strategy" but does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The models are trained using the Adam optimizer (Kingma and Ba 2014) with β1 = 0.9 and β2 = 0.999. The batch size is set to 8. The learning rate is initially set to 2e 4 and decreased to 1e 6 gradually using the cosine annealing decay strategy (Loshchilov and Hutter 2016). For data augmentation, the cropped patches of size 256 256 are randomly horizontally flipped with a probability of 0.5. FLOPs are measured on 256 256 patch size. |