Hybrid Frequency Modulation Network for Image Restoration
Authors: Yuning Cui, Mingyu Liu, Wenqi Ren, Alois Knoll
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on nine datasets demonstrate that the proposed network achieves state-of-the-art performance for three image restoration tasks, including image dehazing, image defocus deblurring, and image desnowing. |
| Researcher Affiliation | Academia | 1Technical University of Munich 2Shenzhen Campus of Sun Yat-sen University {yuning.cui, liumi, knoll}@in.tum.de, renwq3@mail.sysu.edu.cn |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks, but rather architectural diagrams and mathematical formulations. |
| Open Source Code | Yes | The code and models are available at https://github.com/c-yn/CSNet. |
| Open Datasets | Yes | We evaluate our model on both daytime and nighttime dehazing datasets. For daytime scenes, the numerical results on a synthetic (SOTS [Li et al., 2018]) and three real-world datasets, i.e., Dense-Haze [Ancuti et al., 2019], NH-HAZE [Ancuti et al., 2020], and O-HAZE [Ancuti et al., 2018]... DPDD [Abuolaim and Brown, 2020] dataset... CSD [Chen et al., 2021] and Snow100K [Liu et al., 2018]. |
| Dataset Splits | No | The paper mentions training on '256 × 256 patches' and using specific datasets for training and testing, but it does not provide explicit details about the train/validation/test splits of these datasets (e.g., percentages or counts). |
| Hardware Specification | Yes | All experiments are carried out on an NVIDIA Tesla A100 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify its version or the versions of other software libraries or frameworks used. |
| Experiment Setup | Yes | Specifically, the model is trained using the Adam optimizer on 256 × 256 patches with a batch size of 8. The initial learning rate is 2e-4, which is gradually reduced to 1e-6 with cosine annealing. We adopt the horizontal flips for data augmentation. According to the complexity of different tasks, we set N (Figure 3 (b)) to 3 for dehazing and desnowing, and 15 for deblurring. |