隨著顯示器的快速發展，高動態範圍影片的應用也跟著增加。然而，大部份的研究都專注在產生單一的高動態範圍影像，高動態範圍影片則較少被注意到。為了取得高動態範圍影片，直接拍攝高動態範圍影片是可能的。但這需要用到特別的相機，而這類型的相機昂貴且不常見。有一個實際的方法是利用單一相機交替轉換每個幀的曝光時間。這個方法已經被研究一陣子了而且在現有的研究中很多都是以此為基礎。對於大部份的人來說，這個方法是可行的但並不是所有相機都可做到這件事。為了應用現有的這些方法來產生高動態範圍影片，輸入必須要是輪替曝光時間的幀。
為了避免硬體設備的限制，我們提出了一種基於區塊的方法，該方法僅使用單一曝光的低動態範圍影片來重建場景中移動的物件並產生高動態範圍影片。我們計算兩兩相鄰的幀之間的動作流並且對每個動作流分配適當的搜尋範圍。有了動作流和搜尋範圍圖，我們可以重建出具有複雜動作的區域並且不產生錯誤而確保了相鄰幀之間時間上的一致性。比較了目前在產生高動態範圍影片中最先進的Kalantari等人的方法，我們可以減少總執行時間的百分之十四。而且，多種評分方式呈現了我們的結果相較於他們的更接近於參考的影片。

As the display devices develop rapidly, the applicability of high dynamic range (HDR) video is increased. However, most of the researches focus on generating a single HDR image and HDR video has less attention. To obtain HDR video, it is possible to capture HDR video directly. But using a specialized camera is necessary and this kind of camera is expensive and less widespread. A practical way is to use a single camera that alternates exposures for each frame. This approach has been explored in the past and many existing researches are based on it. For most people, this approach is feasible but not all video cameras can do this. To apply existing methods to generate HDR video, the input must be alternative exposure video sequences.
To avoid the limit of equipment, we propose a patch-based method using single low dynamic range (LDR) video to reconstruct moving objects in the scene and generate HDR video. We compute the motion flows in two adjacent frames and assign an appropriate search window size for each flow. Using the motion flow and search window map, we can reconstruct the region with complex motion without generating artifacts and ensure temporal coherence between adjacent frames. Comparing with Kalantari {\it et al.}’s method, the state-of-the-art for producing HDR video, we can reduce 14 percent of total execution time. Also, several evaluations show that our results are more similar to the reference video than theirs.

1 Introduction 1
1.1 Background of high dynamic range (HDR) video . . . . . . . . . . . 1
1.2 Motivation and Problem Description . . . . . . . . . . . . . . . . . 2
1.3 Goal and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Related Works 4
3 Patch-based moving object reconstruction 10
3.1 Generation of pseudo-exposure frame . . . . . . . . . . . . . . . . . 11
3.2 Motion flow estimation . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Original Search window map computation . . . . . . . . . . . . . . 14
3.4 Modified Search window map computation . . . . . . . . . . . . . . 14
3.5 Frame Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Experimental Results 27
4.1 Visual effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2 SSIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.3 Contrast perception error . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 Execution time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5 Conclusion 46
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

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