超解析度方法與系統設計比較研究

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張翔珳(Hsiang-Wen Chang) 查詢紙本館藏

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資訊工程學系

論文名稱

超解析度方法與系統設計比較研究
(Comparative Study for Super-Resolution Methods and System Design)

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摘要(中)

在視訊監控、視覺檢測領域，傳統攝影機因為解析度不足，造成監控品質、檢測率降低，而超解析度方法能夠超越攝影機物理極限，將原低解析度影像插補轉換為高解析度影像，增加系統應用的效能。本論文以三種基於不同原理的超解析度方法Bicubic spline、APNN、SRCNN作為比較的對象，以人臉辨識，條碼辨識來評估和量化監控品質、檢測率的改善程度，以及針對演算法實作後成本進行全面評估，比較每個演算法在人眼視覺品質、記憶體使用量、即時性、適合硬體化、相對耗電量、硬體資源的成本，以提供嵌入式系統開發在選擇超解析度方法時，能夠有量化數據做為參考。從實驗結果得知效果方面，超解析度方法能夠改善系統的辨識率，其中以SRCNN改善幅度最大，超解析度方法也進一步降低資料傳輸頻寬，在成本方面，Bicubic spline有少量資源和計算速度快的特性，適合實作於嵌入式軟硬體，APNN需要相對較多的資源，適合實作於嵌入式硬體，SRCNN需要龐大的硬體資源，故目前僅適合實作於GPU平台，其中，本論文對SRCNN硬體部分做硬體最佳化，在邏輯閘數量和精準度做取捨，達到演算法近似計算，本論文找到兩組解，將SRCNN常數權重乘法器硬體化簡，一組為降低0.79%的邏輯閘，平均特徵差增加0.000120，一組為降低12.2%的邏輯閘，平均特徵差增加0.264911。

摘要(英)

In the field of video surveillance and vision detection, traditional cameras have low video quality and detection rates, owing to their low resolution. To increase the effectiveness of system applications, the super-resolution method was developed to convert low-resolution images into high-resolution images. In this paper, three super-resolution methods (Bicubic spline, APNN, and SRCNN) are compared and evaluated through two experiments to assess their performance in areas such as face and barcode recognition. The properties of each algorithm, including human vision quality, memory usage, execution time, hardware complexity, relative power consumption, and hardware resources, are also discussed. According to the experimental results, system analyzers can choose the appropriate super-resolution method for embedded system development. Our results show that SRCNN has the greatest improvement of recognition rate among the super-resolution methods. In terms of cost, the Bicubic spline method is suitable for embedded software and hardware applications due to its low cost and high speed. APNN can be applied in embedded hardware applications because of its low resource usage. Due to the high resource usage of SRCNN, it is only suitable for certain GPUs. Therefore, we use a genetic algorithm to obtain a trade-off between hardware cost and accuracy to compute an approximation for the SRCNN hardware. We found two approximated results for SRCNN: a 0.79% decrease in hardware cost and an increase of 0.000120 in average feature difference or a 12.2% decrease in hardware cost and an increase of 0.264011 in average feature difference.

關鍵字(中)

★ 超解析度
★ SRCNN
★ APNN
★ Bicubic spline
★ 比較研究

關鍵字(英)

★ Super resolution
★ SRCNN
★ APNN
★ Bicubic spline
★ Comparison Study

論文目次

目錄
摘　要 I
Abstract II
謝誌 III
目錄 V
圖目錄 IX
表目錄 XIII
第一章、緒論 1
1.1 研究背景 1
1.2 研究目標 5
1.3 論文架構 5
第二章、文獻回顧 6
2.1 傳統超解析度方法 6
2.1.1 最近鄰近插補法 6
2.1.2 雙線性插補法 7
2.1.3 雙立方插補法 8
2.2 雙立方樣板插補法 9
2.3 非等向性機率神經網路 13
2.3.1 機率神經網路 13
2.3.2 機率神經網路應用在插補 16
2.3.3 非等向性機率神經網路 17
2.4 超解析度卷積神經網路 19
2.5 評估影像超解析度方法 22
2.5.1 均方差 24
2.5.2 峰值訊噪比 24
2.5.3 雜訊品質測量 25
2.5.4 資訊保真度標準 26
2.5.5 權重峰值訊號雜訊比 27
2.5.6 結構相似性 28
2.5.7 多尺度結構相似性 29
2.6 方法介紹總結 30
第三章、超解析度軟硬體架構設計 31
3.1 嵌入式軟硬體設計與合成方法論 31
3.1.1 IDEF0階層式模組化設計 32
3.1.2 GRAFCET離散事件建模 34
3.2 硬體系統架構 35
3.3 各演算法的GRAFCET 38
3.3.1 Bicubic spline軟體版本 38
3.3.2 Bicubic spline硬體版本 39
3.3.3 APNN軟體版本 42
3.3.4 APNN硬體版本 44
3.3.5 SRCNN軟體版本 46
3.3.6 SRCNN硬體版本 49
第四章、實驗結果 57
4.1 實驗軟硬體介紹 57
4.2 條碼辨識實驗 58
4.2.1 條碼實驗流程 59
4.2.2 條碼影像資料集 59
4.2.3 條碼辨識結果 61
4.3 人臉辨識實驗 65
4.3.1 人臉辨識實驗流程 65
4.3.2 人臉辨識資料集 66
4.3.3 人臉辨識結果 67
4.4 人眼視覺品質評估 71
4.4.1 人眼視覺評估流程 71
4.4.2 人眼視覺評估資料集 72
4.4.3 品質評估結果 74
4.5 嵌入式性能評估 86
4.5.1 演算法性質 86
4.5.2 記憶體使用量 88
4.5.3 即時性 89
4.5.4 適合硬體化 90
4.5.5 耗電量 93
4.5.6 性能評估結果 94
4.6 硬體資源 95
4.6.1 硬體合成和驗證 95
4.6.2 Latency 105
4.6.3 硬體資源 106
4.7 SRCNN硬體最佳化 108
4.7.1 常數乘法器 108
4.7.2 基因演算法 110
4.7.3 近似計算 113
4.7.4 硬體最佳化 115
4.7.5 實驗總結 117
4.8 綜合比較結果 118
第五章、結論與未來發展 120
5.1 結論 120
5.2 未來展望 122
參考文獻 123

參考文獻

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2017-11-30

推文