Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
Authors: Man Zhou, Jie Huang, Chun-Le Guo, Chongyi Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our paradigm, Fourmer, achieves competitive performance on common image restoration tasks such as image de-raining, image enhancement, image dehazing, and guided image super-resolution, while requiring fewer computational resources. Our contributions are summarized as follows: (1) We propose a global modeling paradigm for image restoration that balances effectiveness and efficiency in comparison to existing global modeling-based frameworks. (3) Our paradigm Fourmer achieves competitive performance on several mainstream image restoration tasks, such as image de-raining, enhancement, dehazing, and guided super-resolution, while requiring fewer computational resources. |
| Researcher Affiliation | Academia | 1S-Lab, Nanyang Technological University, Singapore 2 Department of Automation, University of Science and Technology of China, Hefei, China 3School of Computer Science, Nankai University, Tianjin, China. |
| Pseudocode | No | The paper includes architectural diagrams and mathematical equations, but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for Fourmer is publicly available at https://manman1995.github.io/. |
| Open Datasets | Yes | Low-light image enhancement. We evaluate our paradigm on two popular low-light image enhancement benchmarks, including LOL (Chen Wei, 2018) and Huawei (Hai et al., 2021). LOL dataset consists of 500 low-/normallight image pairs and splits 485 for training and 15 for testing. Huawei dataset contains 2,480 paired images and splits 2,200 for training and 280 for testing. |
| Dataset Splits | No | The paper provides details on training and testing splits for datasets, but does not explicitly mention a separate validation split or its size. |
| Hardware Specification | No | The paper states that the method requires fewer computational resources but does not provide specific details about the hardware used for experiments (e.g., GPU models, CPU specifications). |
| Software Dependencies | No | The paper mentions the use of FFT/IFFT algorithms but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Optimization Flow. In addition to the novel network designs, we also introduce a new loss function for optimizing the network training for better results in both spatial and frequency domains. The new loss function consists of two parts: a spatial domain loss and a frequency domain loss. In the spatial domain, we adopt the L1 loss function, as expressed in Equation (3). ... Finally, the overall loss function is formulated as L = Lspa + λLfre, (5) where λ is the weight factor and is set to 0.1. |