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+# Rethinking Noise Synthesis and Modeling in Raw Denoising
+
+[Yi Zhang](https://zhangyi-3.github.io/)<sup>1</sup>,
+[Hongwei Qin](https://scholar.google.com/citations?user=ZGM7HfgAAAAJ&hl=en)<sup>2</sup>,
+[Xiaogang Wang](https://scholar.google.com/citations?user=-B5JgjsAAAAJ&hl=zh-CN)<sup>1</sup>,
+[Hongsheng Li](https://www.ee.cuhk.edu.hk/~hsli/)<sup>1</sup><br>
+
+<sup>1</sup>CUHK-SenseTime Joint Lab, <sup>2</sup>SenseTime Research
+
+
+
+## Abstract
+
+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.
+
+## Code & calibrated parameters
+
+coming soon ！
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