在影像處理與電腦視覺領域中，影像雜訊模型是很重要的問題，過去有很多
透過統計方法制定的雜訊模型，但這些方法仍然與現實世界的雜訊存在差距。
為了解決這樣的問題，在本論文中，我們提出一套透過資料驅動的方法，從
有限的清晰與雜訊的成對影像資料中，學習生成雜訊的模型。我們提出的方
法可以做到感知不同的相機，並一次性地學習出代表不同相機雜訊的特徵，
進而生成專屬於某種相機的雜訊。從實驗結果可以清楚發現，我們的方法在
效能評比數據及生成雜訊影像品質上，都超越了目前既有的統計模型與基於
深度學習的雜訊模型。

Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods.

List of Tables 5
List of Figures 6
摘要 8
Abstract 9
Introduction 10
Related work 12
Our Approach 14
Experiments 20
Application to Real Image Denoising 32
Conclusion and Future Work 35
Bibliography 36

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