Several new techniques for image inpainting

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Thesis > Item 987654321/35910

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Title:	Several new techniques for image inpainting
Other Titles:	新的影像修補技術
Authors:	陳衍良;Chen, Yen-liang
Contributors:	淡江大學電機工程學系博士班謝景棠;Hsieh, Ching-tang
Keywords:	影像修補;多重解析;小波轉換;適應分解;修補優先權;浮水印;扭轉;自相似性匹配;細帶轉換;幾何流向;Image Inapainting;multi-resolution;Wavelet transform;Adaptive Decomposition;Repairing Priority;Watermark;Warp Transform;Affine Matching;Bandelet Transform;Geometrical Flow
Date:	2009
Issue Date:	2010-01-11 07:18:43 (UTC+8)
Abstract:	本論文根據破壞區域周圍的人類視覺感官特性所對應多重解析維度，提出適應性的影像修補演算法。我們探討適應性的多層解析分解、修補優先權的順序及不同像素修補決策法等等技術對輪廓及紋理產生的影響，依序提出四種影像修補技術： 1. 漸進式影像修補法：以小波轉換為基礎的數位影像修補演算法，即利用二階小波轉換將待修補影像分解至低、中、高三個不同頻率成分之小波層，進行影像修補工作。首先由低頻率小波層進行粗略解析之影像輪廓預測，以該層所獲得之粗略輪廓修補。依該修補結果為依據，漸漸提昇至中、高頻率小波層，進行更精細的紋理修補，使修補結果更接近人類視覺感官。 2. 適應性分解影像修補法：為了解決在大破壞區域的錯誤修補結果，提出適應性階層小波轉換之相似性數位影像修補演算法。依破壞區域的大小，決定相對應小波階層數以進行適應性分解，提高粗略輪廓的預測修補的正確性；並且根據同一影像中具有相似性輪廓及紋理的特性，提出自相似影像修補決策法進行修補。 3. 幾何流向為依據細帶修補法：雖然小波轉換可將影像適應性分解至不同小波解析層，但對不同走向之紋理成分無法有效分解，導致修補結果不夠細膩。為了改善此缺點，我們提出以Bandelet轉換為基礎之修補演算法。利用Bandelet轉換取得輪廓及紋理之幾何流向的資訊，再依此幾何流向進行數位影像修補，即可獲得更精細的修補結果。 4. 浮水印為依據修補法：若大破壞區域同時包含不同輪廓及紋理變化的物件時，將無法利用有限輪廓資訊進行粗略影像修補工作。為了解決這個問題，我們利用強健的影像輪廓浮水印技術，提供原影像約略的輪廓走向資訊，使修補法有所依循。再利用適應性多重解析層進行細膩修補。如此可以避免因粗略輪廓錯誤，而造成視覺上嚴重的整體修補錯誤。因此，本論文針對不同的破壞區域大小及紋理形式經由上述所提出的修補演算法進行實驗。實驗結果顯示：如果能夠對待修補影像進行分解至不同解析層，甚至依據不同大小的破壞區域能夠適應性的徹底解析，即可減少各層資訊的複雜度，以利漸進式修補法有效進行分析及決策。其次，適應性小波分解雖然提供足夠之小波分解層數，但Bandelet 轉換比小波轉換對輪廓及紋理走向更能有效描述，在各層小波係數更具有集中性；在該層的影像成分進行修補，其修補的結果更能提高細膩。最後，若將預先儲存於原影像中之浮水印做為修補參考，則對大破壞區域的影像有助於提高原始影像的重建率。 In this paper, we proposed the adaptive inpainting method according to the multi-resolution of nearing damaged district of human visual characteristics. We explore the impacts of these techniques of the decomposition, the priority in decision-marking and repair techniques on result of image inpainting. Form this, we proposed the four methods for restoration of damaged images. 1. Progressive image inpainting: The digital image inpainting based on wavelet transform. This is, using the two-level wavelet transform to decomposition the image into three wavelet layers of different frequency components (low, middle and high) to carry on image inpainting procedure. First, contour estimation with coarse resolution is conducted on the low frequency wavelet layer, and the image is repaired according to the obtained coarse contour. Based on the repairing results, the wavelet layers are progressively repaired, gradually moving from lower to higher frequencies to carry out finer texture repair and producing results that are more consistent with the human visual perception. 2. Adaptive decomposition inpainting: In order to resolve the issue of false repair results at sites with big damage district, we propose to perform adaptive decomposition of wavelet transform. The size and extent of the damaged region are evaluated to obtain the corresponding wavelet layers for carrying out adaptive decomposition of the image. By examining the similarity in contour and texture in the same image, self-similarity decision-making rules are then proposed to conduct image repair. 3. Geometric Bandelet Inpainting: Although wavelet transform allows decomposition of an image into different resolution layers, it cannot achieve perfect decomposition on two-dimensional images. Therefore, if repair is conducted directly using the wavelet coefficients, the resulting image will not achieve the desirable refined quality. To overcome this and to acquire satisfactory image repair results, we propose to carry out image repair by taking advantage of the concept of bandelet transform, as well as the geometric flow of image contour and texture. 4. Watermark Inpainting: In the case when the damaged region contains different multiple objects, the limited contour information will not allow image repair to be carried out correctly. To solve this problem, the image contour watermark previously embedded in the image is used as a reference to guide the image repair work. Thus, this method for repairing damaged images is based on the analysis of image watermark. In this thesis, we investigated restoration of damaged images using the four kinds of methods described above. The four methods are distinguished by their applicability to damaged regions of various sizes and textures. From our experimental results, we discovered that we can successfully decompose an image with large-scale damage into different resolution layers and even adaptively decompose the image according to the size and extent of damage. We were able to obtain sufficient number of image analysis layers and reduce the complexity of information in each layer to enable effective and progressive repairing on damaged images. In addition, by using bandelet transform, we were able to adaptively decompose damaged images according to the trend in their contours and textures, making the distribution of coefficients in each layer more concentrated and allowing finer repair results to be obtained. We also found that we can significantly increase the reconstructability of damaged images if the contour watermark of the original image is used as a reference for conducting image repair.
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