使用生成對抗學習的全卷積網路移除影像中的外嵌文字;Removing Embedded Text in Images via Fully Convolutional Networks with Generative Adversarial Learning

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/74759

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/74759

Title:	使用生成對抗學習的全卷積網路移除影像中的外嵌文字;Removing Embedded Text in Images via Fully Convolutional Networks with Generative Adversarial Learning
Authors:	陳書恆;Chen, Shu-Heng
Contributors:	資訊工程學系
Keywords:	影像修復;深度學習;生成對抗網路;image inpainting;deep learning;generative adversarial network
Date:	2017-08-22
Issue Date:	2017-10-27 14:38:29 (UTC+8)
Publisher:	國立中央大學
Abstract:	影像加上文字是網路上最普遍被使用的媒介之一。舉例來說，網民會製作大量的梗圖 (memes) 使用在許多的目的上。然而在某些情況下，這些外加的文字會破壞影像的美觀而且增加其他應用的難度，像是場景的辨識、物體的分類…等。因此，本研究主要的目標是提出一個能夠自動清除影像中外嵌文字並補全影像的系統。隨著新世代電腦技術的發展，深度學習技術可以應用在影像處理技術上並且表現優於傳統的影像處理方法。在我們提出的系統中，為了獲得更佳的結果，我們利用最新的深度學習框架，建立了兩個模組：文字遮罩生成模組和影像補全模組。文字遮罩生成模組用來自動偵測給定影像中的嵌入文字，再輸出對應的遮罩。影像補全模組則是將受汙染的影像和對應的遮罩影像作為輸入，然後產生修補後的影像。我們透過實驗與兩種已經成熟發展的非深度學習的影像修補技術進行比較。結果顯示我們提出的方法比傳統的影像修復技術，修補後的影像更自然且更少瑕疵。 ;An image embedded by texts is one of the most common 2D media in the web; for example, the netizen produce lots of this kind pictures or memes for different purposes. In some situations, the added texts make a beauty picture into a garbage. For example, we cannot use the image for some other purposes, such as scene recognition, object classification, …, etc. Therefore, in this study, we aim to propose a system that can clean texts automatically on a given image and inpaint or restore the image. With novel generation of computer technology, the deep learning architecture can be applied on the inpainting problem and perform better results than several traditional methods. In the proposed system, we construct two modules using the latest and novel deep learning frameworks to get a great result. The first module, mask generation module, is used for detecting the embedded texts in a given image automatically and products the corresponding bitmap image mask. The second module, image completion module, can inpaint the corrupt images based on the given mask image. In the experiments, we compare our results with two fully developed and without deep learning technique methods. We show that the proposed method can provide more natural and less flawed results than the classic image inpainting methods provided.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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