利用多目標生成對抗網路去除單張影像反射現象

去除影像中的反射現象一直是影像處理與電腦視覺中公認的難題之一。然而其所帶來的影響，輕則讓留有瞬間珍貴回憶的相片留下瑕疵，嚴重甚至會影響電腦視覺系統的運作，如監視系統等。因此一個可於合理時間與空間成本下運行之有效去反射的方法成為了許多研究者想要追求的目標。在這本篇論文中，我們提出一個基於多目標生成對抗網路的學習架構來學習如何在合理的時間內盡可能地去除影像中的反射影像。透過修改原始生成對抗網路之損失函數與加入多目標學習架構，並配合資料生成模型產生出足夠支持學習架構之資料集使網路能學習到具母體代表性之參數組合。相較於現有以最佳化為基礎的去反射演算法，我們的方法有兩大優勢:非常快的執行速度與超越現有方法之去反射結果。透過生成對抗網路的特性，我們成功地在去除反射的同時，保留了其他方法無法留下的材質與細節，大大提高了結果影像的品質。

關鍵字

影像去反射；生成對抗網路；多目標學習

並列摘要

Removing reflection from the image is one of the hardest problem in the computer vision and image processing. However, its impact to our precious photos or computer vision system, like surveillance camera, is significant and desired to be solved. Therefore, we aimed to find out a practical approach to accomplish the removal in reasonable time and space. In this thesis, we proposed a new network architecture called Multi-Task Generative Adversarial Network. We trained network to learn how to remove reflection as much as possible and in proper time as well. In order to support learning, we proposed a data synthesis model that synthesize realistic reflection-containing images. By modifying loss function and imposing multi-task architecture into network, we expected our network can learning the parameters to separate reflection layer from background. Compared to major optimization-based algorithms, our network had two advantages: fast and effective. We successfully removed reflection and kept the texture and detail as well. By taking the advantage of Generative Adversarial Network, the quality of result has been significantly improved.

並列關鍵字

Image Reflection Removal ； Generative Adversarial Network ； Multi-Task Learning

參考文獻

[1] A. Levin and Y. Weiss. User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 29(9):1647–1654, 2007.

[5] N. Arvanitopoulos, R. Achanta, and S. Süsstrunk. Single Image Reflection Suppression. In Computer Vision and Pattern Recognition (CVPR), 2017.

[6] Y. Li and M. S. Brown. Exploiting Reflection Change for Automatic Reflection Removal. In IEEE International Conference on Computer Vision(ICCV), pages 2432–2439, 2013.

[8] T. Xue, M. Rubinstein, C. Liu, and W. T. Freeman. A Computational Approach for Obstruction-free Photography. ACM Transactions on Graphics, 34(4):79:1–79:11, 2015.

[9] C. Sun, S. Liu, T. Yang, B. Zeng, Z. Wang, and G. Liu. Automatic Reflection Removal Using Gradient Intensity and Motion Cues. In ACM Multimedia, MM ’16, pages 466–470, 2016.

國際替代計量

利用多目標生成對抗網路去除單張影像反射現象

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