We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| rockwenjj/syreanet | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_beta_D | Ran | rockwenjj/syreanet/synthesize/synthesize.py pointer only (licence: NONE) · get_code("0ed9f9a397c1e5e6") |
| estimate_backscattering | Ran | rockwenjj/syreanet/synthesize/synthesize.py pointer only (licence: NONE) · get_code("57d3e8db8fbb61c9") |
| scale | Ran | rockwenjj/syreanet/synthesize/synthesize.py pointer only (licence: NONE) · get_code("93beef316b1f1451") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.08269")
get_code_for_paper("2302.08269")
have("2302.08269")
Connect an agent — have() is free.