在三維數碼城市中，為了更為符合真實世界，通常都會敷貼牆面紋理影像在模型上，然而在近景拍攝的過程中經常會受到一些樹木、車子、及其他物體的遮蔽影響。因此，有效地去除及修補遭遮蔽的牆面紋理圖像是一個值得研究的課題。數位影像修復是一種移除遮蔽並填補數位影像的有效方法，然而將現有的影像修補技術直接應用於牆面紋理，對於遮蔽的處理並不理想。因此本研究研發一個改良的影像修補技術以處理受遮蔽的牆面紋理，目的是將選擇的遮蔽物移除並藉由此技術找出可靠的紋理回填受損的區域。本研究所研發的影像修補結合了等照度面、法向量、可靠度計算等，計算影像填補的優先順序，並合理地修復受遮蔽的紋理影像。 與現有的影像修補相比，本研究提出的修補技術是基於房屋牆面紋理上的明顯結構，如樑、柱、窗子等以更合理的修復遮蔽。一些重要的參數如搜尋視窗、搜尋範圍也經深入探討以測試影像修補的成果；此外由於牆面紋理有沿著特定方向的特性，因此限定影像修補沿著特定的方向測試其成效。本研究所研發的改良式影像修補技術以真實牆面紋理影像進行測試，並確立影像修補參數的設定。最後，以均方根誤差和結構相似度等指標，進行牆面紋理影像遮蔽修補成果之量化評估。本研究測試不同類型之房屋牆面紋理修復，並以多視角呈現建物群體影像修復，實作成果顯示本研究所提出以房屋牆面紋理上明顯結構特徵為基本單元之方式，可有效修復受遮蔽的牆面紋理影像。 In three dimension digital city modeling, photographic fa?ade texture images are commonly attached to building faces in order to generate more realistic building models and scenes. However, there are usually occlusions from trees, cars and other foreign objects in close-ranged fa?ade photographs. Therefore, it is worthwhile to develop effective methods for efficiently correcting the occlusions in fa?ade texture images. Image inpainting is an effective approach to remove unwanted objects and fill holes in digital images. However, directly applying existing, general-purpose digital inpainting algorithms to the correction of fa?ade texture images with occlusions may not produce satisfactory results. This research developed a constrained image inpainting algorithm specifically designed for the correction of occluded fa?ade texture images. The objective is to remove selected occlusions of fa?ade texture images and restore the damaged texture blocks with reasonable textures identified with the developed constrained inpainting algorithm. The developed inpainting algorithm combines isophote, normal vector and confidence terms to calculate the best fill order of inpainting. In comparison with existing inpainting approaches, it can restore the occluded texture blocks more reasonably based on fa?ade structures such as pillars, girders and window frames. A few parameters, such as window size and search area of inpainting were also investigated to better understand their effects on the inpainting results. Special constrains on the direction of inpainting order, such as top-down and bottom-up were also tested to understand their effects because the features of texture fa?ade were presumed to be along a particular direction. The developed constrained inpainting algorithms were applied to real building fa?ade images to valid their performance and to identify appropriate inpainting parameters for correcting fa?ade texture occlusions. Finally, the most widely used full-reference quality metric mean square error (MSE) and structural similarity index (SSIM) were employed to quantitatively evaluate the inpainting results. Several complex building models are used to test the constrained image inpainting algorithms. Experimental results demonstrate that the proposed method based on fa?ade structures is effective in restoring occlusion of building fa?ade texture images. In addition, the experimental results of building fa?ade texture images with special structures are also validated, proving that the proposed approach can restore more reasonable visualization results.
