超解析度重建(superresolution)，定義上為利用一組低解析度影像來增進影像的時間或空間解析度的一種訊號演算法，其過程包括影像放大及交疊現象、雜訊和方塊效應的消除。對視訊序列的超解析度重建，兩個最主要的關鍵議題是：高度的計算複雜度以及存在於每一畫面的遮蔽問題所導致的對位(registration)誤差，尤其是畫面場景變更此一極致現象。於本論文中，作者採用交叉鑽石六角搜尋(Cross-Diamond-Hexagonal search, CDHS)配合區塊匹配演算法來有效降低計算複雜度。實驗結果顯示，CDHS區塊匹配演算法較之全搜尋區塊匹配演算法，雖可節省約 的計算時間，且其重建結果亦可堪比，唯因遮蔽問題，二者之重建結果皆不甚理想，故作者進一步提出一簡單且強靭的智慧區塊移動向量篩選(block-wise motion vector selection, BWMVS)的策略，此BWMVS策略能有效的解決遮蔽問題。BWMVS策略除簡易之外，透過各種內插(interpolation)方法的驗證，亦顯示其非常強靭穩定。由實驗結果可知，CDHS區塊匹配演算法和BWMVS策略的結合運用，不管視訊序列是否壓縮，甚至在高壓縮比之下，皆可在不複雜的計算下得到高度的重建品質。
　　雖然由單張影像來做重建並不符超解析度重建的定義，唯吾人常面臨只有單張影像卻需做超解析度重建的情形。不同於視訊序列的超解析度重建，單張的超解析度重建過程，其重要關鍵在於影像邊緣的保留。修伯-馬可夫隨機場(Huber Markov random field, HMRF)模型對重建影像邊緣的保留及雜訊抑制有不錯的效果，一般在HMRF模型的事前機率模型中使用的是傳統高通濾波器，於論文中作者提出一小波模組(wavelet modulus)來取代此一傳統高通濾波器。實驗結果顯示，此小波模組比傳統高通濾波器更具邊緣保留的能力及雜訊抑制，因此有更佳的重建結果。此小波模組亦嘗試用於視訊序列的超解析度重建，與使用傳統高通濾波器者比較，然效果並不顯著，只有在高度雜訊環境中才可看到些許的改善，原因在於影響視訊序列超解析度重建的關鍵是如上述提及的對位誤差。

Superresolution (SR) is a signal processing algorithm that endeavors to increase spatial or temple resolution of images from a set of low resolution (LR) observations. It involves up-conversion of the input sampling lattice (pixel) as well as reducing or eliminating aliasing, blocking artifact, and blurring. High computational complexity and occlusion including the extreme case of a scene change are the two most critical problems in superresolution reconstruction from video sequences. In this thesis, we adopted the Cross-Diamond-Hexagonal Search (CDHS) block-matching algorithm (BMA) to reduce the complexity significantly. The CDHS BMA reduced about computation time without sacrificing the reconstruction quality comparable to the full search BMA but it has high noises, unqualified motion vectors, resulted from occlusion existing in each frame of a sequence. We further proposed a simple and robust block-wise motion vector selection (BWMVS) strategy that can remove occlusion effectively. The stability due to BWMVS was verified by the reconstruction results using the standard interpolations. The experimental results showed that by combining the CDHS BMA and BWMVS, high quality images in SR reconstruction can be obtained without complex computation.
The reconstruction from a single image does not satisfy the definition of SR reconstruction. However, we usually face the condition to reconstruct a SR image but only one low resolution image can be given. The critical issue in SR reconstruction from a single image is the edges preserving which is different from the registration errors intensively discussed in the reconstruction from video sequences. In this thesis, a proposed wavelet modulus replacing the conventional high pass filter was implemented in the a priori model based on Huber Markov random field (HMRF) to enhance the edges preserving ability. The experimental results show that the proposed wavelet modulus has a better performance than the conventional high pass filter. Of course, the HMRF was also implemented in the cases of reconstruction from video sequences to avoid the reconstructed images to be over-smoothness. As compared to the conventional high pass filter, the reconstructed results by the proposed wavelet modulus show that there is only little improvement while the image is under high noise environment. This is because the registration errors play the main role in the SR reconstruction from a video sequence as cited above.

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