在本篇論文中，我們提出了一個以整合型的最佳化框架同時進行立體影像校正及光學失真修正的新穎的演算法。雙眼立體視覺依賴校正後的影像組合來從兩張影像中的對應點估測深度資訊，也就是立體匹配演算法。雖然許多研究專注於立體匹配演算法並且也得到不錯的結果，立體影像校正仍是一個重要但卻被忽視的主題來產生必要的校正影像。
在多解析立體視覺系統中，立視影像校正更顯得重要。這個系統由兩個不同的兩個鏡頭組合成一個低成本的立體相機組。在這個系統裡得到的影像組合，不同的影像品質、雜訊及光學失真程度都可能導致深度估測錯誤。
考慮不同的內部相機參數與外部參數，一組單應性轉換可以由極線限制估測出來。光學失真也由除法失真修正模型和維度提升的方法加以考慮。使用非線性最佳化的架構配合穩健估測函式可以同時達到這兩個目標。
在合成與真實影像的實驗結果中，所提出的演算法不只在避免校正失真的情況下有效地減少了校正錯誤，也能對影像中的光學失真現象加以修正。此外，我們的實驗結果顯示提出的方法可以引導出更精確的視差圖估測結果。

In this thesis, we propose a novel algorithm to simultaneously solve the stereo rectification and distortion correction problems in an integrated optimization framework. Stereo vision estimate depth information from a rectified image pair by finding point correspondences across images, i.e. stereo matching. Previous researches focused on developing accurate stereo matching methods. However, stereo rectification has been a crucial but overlooked topic to generate rectified images for stereo matching.
The stereo rectification is especially important when the stereo images are acquired by different cameras with considerable lens distortions, which may lead to errors in image rectification and depth estimation.
Considering different intrinsic and extrinsic parameters for stereo cameras, rectification homography transforms for both images are estimated based on the epipolar constraints. Lens distortion is also included into the generalized epipolar constraints by employing a division undistortion model and a lifting technique. By using the robust estimation formulation, we can solve the stereo rectification and distortion correction problems simultaneously in a unified framework.
Experimental results on various synthetic and real images are shown to demonstrate that the proposed algorithm not only effectively reduces the rectification errors but also corrects the lens distortion in the stereo images. In addition, our experiments show that the proposed stereo rectification algorithm leads to more accurate disparity estimation results.

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