基於分析邊緣視差之影像反光移除

在日常攝影時，當場景中具有玻璃或者光滑平面時，很容易便會將其所造成的反光一併照至相片內。儘管這是很普通的自然現象，但對於照相者而言這些反光卻可能會影響到自己原先所期望拍攝的成果，是不理想的。因此，如何將影像中的反光移除一直對照相者造成很大的困擾。本文中，我們嘗試使用雙眼照片來解決這個問題。如同人左右眼一般，深度不同的物體在雙眼相機所照下的左右兩張圖片中具有不同的位置差距。這些差距正是讓我們產生立體感的關鍵所在。而因為物體的反光相當於是在反射面的另一頭製造出一個與此物體和反光面距離相等的反光層，因此在影像中，反光也同樣具有其自身的深度差距。我們先對左右兩張圖片的影像梯度分別進行立體匹配計算之後，依照反光所具有的深度差距特性將影像邊緣分為反光層和背景層兩類，再藉由圖層分離演算法將單張圖片分出反光層和背景層之後，利用 SIFT 的光流軌跡將兩張圖片對齊平均後得到最後的結果。

關鍵字

反光；反光移除；影像深度差距

並列摘要

When taking photos, if there is glass or reflected plane in a camera scene, reflections may occur in the photos. And for photographers, there reflections could have bad effects to their ideal images. Thus, how to remove reflections has been a common problem in image processing. In this thesis, we try to solve this problem by a pair of binocular images. Like human eyes, images taken by a stereo camera have different disparity values on objects of different depths. As the reflection layers could be regarded as a layer inside the reflected plane, they also have their own depths and disparity values. By these stereo information, we can tell where the reflections are. Here, we first apply stereo matching to image gradients of the both left and right images. Then we label image edges by analyzing their disparity values. With the help of these labels, we can do layer separation to get the reflection layers and the background layers for the both images. Finally, we use SIFT flow for aligning these two images and combine them to get the final result.

並列關鍵字

reflection ； reflection removal ； image disparity

參考文獻

[1] A. Levin, A. Zomet, and Y. Weiss, “Separating reflections from a single image using

Proceedings of the 2004 IEEE Computer Society Conference on, vol. 1, June 2004,

[2] A. Levin and Y. Weiss, “User assisted separation of reflections from a single image

[3] H. Hirschmuller, “Accurate and efficient stereo processing by semi-global matching

and mutual information,” in Computer Vision and Pattern Recognition, 2005. CVPR

國際替代計量

基於分析邊緣視差之影像反光移除

全文下載

主題瀏覽