本文為澳大利亞莫納什大學（作者：Paul Francis Harrison）的博士論文，共141頁。

本文涉及三種影象紋理操作：從樣本合成紋理，從一幅影象到另一幅影象的紋理轉移，以及不完整或噪聲影象的合理復原。由於人類視覺感知對紋理細節的敏感特性，因此對這些操作產生令人滿意的結果是困難的。本文提出了幾種實現這些操作的新方法。在紋理合成方面，本文對最佳擬合方法進行了改進，消除了該方法有時產生的“歪斜”和“垃圾”效應，同時能夠快速、靈活、簡單地實現，因此是一種非常實用的方法。本文提出了一種基於隨機拼貼的簡單快速技術，這兩種技術都可以適用於將紋理從一幅影象轉移到另一幅影象。接下來，提出了一種非線性預測器形式的基於影象紋理模型的噪聲去除方法，該方法被應用於通過諸如調色處理(例如GIF)之類的壓縮技術實現退化影象的紋理復原和高斯去噪，其效能與基於最新小波技術的方法相當。最後，基於指定形狀的瓦片排列，研究了一種更抽象的紋理合成形式，用於顯示最佳擬合合成的古器物原貌。

Three image texture operations areidentified: synthesis of texture from a sample, transfer of texture from oneimage to another, and plausible restoration of incomplete or noisy images. Ashuman visual perception is sensitive to details of texture, producingconvincing results for these operations can be hard. This dissertation presentsseveral new methods for performing these operations. With regard to texturesynthesis, this dissertation presents a variation on the best-fit method [Efrosand Leung, 1999, Garber, 1981] that eliminates the “skew” and “garbage” effectsthis method sometimes produces. It is also fast, flexible, and simple toimplement, making it a highly practical method. Also presented is a simple andfast technique based on random collage. Both of these techniques can be adaptedto transfer texture from one image to another. Next, a noise removal methodthat is guided by a model of an image’s texture, in the form of a non-linearpredictor, is presented. The method is applied to plausibly restoring thetexture of images degraded by compression techniques such as palettization(e.g. GIF), and to the removal of Gaussian noise, with results comparable tostate-of-the-art wavelet-based methods. Finally, a more abstract form oftexture synthesis is examined, based on the arrangement of tiles of specified shape.This is used to show the origins of the artifacts seen in best-fit synthesis.

1 引言
2 歷史文獻回顧
3 改進的最佳擬合紋理合成
4 引入最佳擬合紋理合成的GIMP
5 拼貼式紋理合成
6 噪聲影象的合理復原
7 宣告性紋理合成
8 結論與未來工作展望
附錄A 測試資料及程式
附錄B WSCG-2001發表的論文

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