Unified Srgb Real Noise Synthesizing with Adaptive Feature Modulation

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)

引用 0|浏览4
暂无评分
摘要
Recently, the Neighboring Correlation-Aware (NeCA) noise model has achieved impressive performance on both noise synthesis and the downstream image denoising task. However, its design regarding noise-level prediction requires training NeCA separately for each camera type. To this end, by making use of an adaptive feature modulation technique, we improve NeCA’s noise-level prediction model to be unified for different camera types and thus enable a unified sRGB real noise synthesis method. We also find out that in the neigh-boring correlation network of NeCA, there is no mechanism to maintain the signal dependency of the synthesized noise. Therefore, we introduce another adaptive feature modulation technique to the neighboring correlation network to maintain the signal dependency of the noise.
更多
查看译文
关键词
Noise modeling,feature modulation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要