Hongsheng Li, Xiaogang Wang, Yi Zhang, Hongwei Qin
We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| zhangyi-3/noise-synthesis | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| noise_profiles | Ran | zhangyi-3/noise-synthesis/synthesize.py code served (permissive licence) · get_code("87bc338739bd05f4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The lack of large-scale real raw image denoising dataset gives rise to challenges on synthesizing realistic raw image noise for training denoising models. However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors. Existing methods are unable to model all noise sources accurately, and building a noise model for each sensor is also laborious. In this paper, we introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors. Two efficient and generic techniques: pattern-aligned patch sampling and high-bit reconstruction help accurate synthesis of spatial-correlated noise and high-bit noise respectively. We conduct systematic experiments on SIDD and ELD datasets. The results show that (1) our method outperforms existing methods and demonstrates wide generalization on different sensors and lighting conditions. (2) Recent conclusions derived from DNN-based noise modeling methods are actually based on inaccurate noise parameters. The DNN-based methods still cannot outperform physics-based statistical methods. The code will be available at https://github.com/zhangyi-3/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.04756")
get_code_for_paper("2110.04756")
have("2110.04756")
Connect an agent — have() is free.