Large block inpainting by color continuation analysis

[[abstract]]Automatic inpainting is a mechanism which repairs damaged pictures using an approximation mechanism. The most difficult problem is to inpaint a large damaged area, without knowing its content. One possible solution is to use color interpolation or extrapolation on surrounding pixels. However, spatial characteristics such as edges and pixel continuations are hard to be restored. In this research, we propose a series of automatic algorithms, which is based on an analysis of color continuations. Large damaged blocks are repaired, before the rest smaller potions are repaired by a multiresolution inpainting algorithm. The mechanism is tested on more than 2000 images, including cartoon drawing, photos, Chinese painting, and western painting. Our results prove that, the proposed automatic mechanism fixes damaged image up to a certain degree of satisfaction from the users. The demonstration of our work is available at: http://www.mine.tku.edu.tw/demos/inpaint.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20040105~20040107[[iscallforpapers]]Y[[conferencelocation]]Brisban, Australi

The Highly Lose Image Inpainting Method Based on Human Vision

[[abstract]]Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20060901~20060901[[conferencelocation]]Beijing, Chin

[[alternative]]Photo Defect Detection and Inpainting

計畫編號：NSC94-2213-E032-017研究期間：200508~200607研究經費：398,000[[sponsorship]]行政院國家科學委員

[[alternative]]Multi-Resolution Image Inpainting

計畫編號：NSC92-2213-E032-030研究期間：200308~200407研究經費：682,000[[sponsorship]]行政院國家科學委員

[[alternative]]Video defect detection and inpainting

碩士[[abstract]]本篇論文提出一個修補影片的系統，其主要的偵測對象是短暫性出現的雜訊。我們利用以區塊為基礎的移動估計，使用SAD（Sum of Absolute Difference）公式計算移動向量，進而偵測出雜訊。接著再將偵測到的雜訊過濾，最後再利用temporal及spatial的資訊雙重修補。Spatial的修補方法是利用多重解析度影像修補（multi- resolution image inpainting）。 本研究的結果，可以透過我們的偵測與過濾的演算法，成功地將毀損的區域偵測並修補；大部分的雜訊都可以成功的被偵測到，偵測率約為90%；而其修補後的結果也非常平滑、影片瀏覽順暢，視覺效果與原本影片非常相近，實作結果很好。[[abstract]]Video inpainting uses spatial-temporal information to repair defects such as spikes and lines on aged films. We propose a series of new algorithms based on adjustable thresholds to repair different varieties of aged films. The main contribution is an automatic spike and dirt detection mechanism. We prove that if appropriate threshold is once decided by the author, almost all damages in an aged video clip can be detected. In addition, the repairing procedure first estimates temporal information and obtain replacement blocks among several frames. Spatial information is then used to repair damages that can not be fixed by temporal information due to fast motion. The results are visually pleasant with most defects removed.[[tableofcontents]]第一章 緒論 1 1.1 研究動機與目的 1 1.2 相關背景 4 1.3 論文架構 12 第二章 相關研究 13 第三章 相關技術 20 3.1 移動估計 20 3.1.1 搜尋的範圍 23 3.1.2 計算最佳區塊的公式 23 3.1.3 搜尋的方法 25 3.2 影像修補 33 第四章 演算法 42 4.1 系統架構 43 4.2 程式流程 45 4.3 程式演算法 48 4.3.1 偵測演算法 51 4.3.2 過濾演算法 56 4.3.3 修補演算法 60 第五章 實驗結果與討論 65 5.1 實作分析 65 5.2 實驗結果 71 第六章 結論與未來方向 82 參考文獻 84 英文論文 A1[[note]]學號: 692190936, 學年度: 9