臺灣博碩士論文加值系統

Image-blurring 技術主要是在處理影像當中的雜訊，由於影像處理過程中可能會受到通道與取樣誤差的影響使得影像出現粗糙以及雜訊，造成人眼感受變差，因此透過影像模糊化可以有效降低影像雜訊。本研究主要針對應用於影像模糊化的一項技術，雙邊濾波器來進行硬體化，以及希望在提高影像品質的前提下，不耗費太多的硬體成本，達成輕量化的目的。首先我們以查表法作為基底來進行exponential計算，由於exponential在硬體上難以實現，因此我們選擇了在計算硬體以及記憶體使用上更少的查表法來實現。另外，我們也針對傳統雙邊濾波器分析了雙邊濾波器的兩個權重，為Similarity weight和Closeness weight，根據我們的演算法也能夠在一定程度上化簡原始公式，達到硬體簡化的目的。
除了提高影像品質外，本研究也注重於降低演算法的運算複雜度與硬體成本，在只有使用Line buffer的前提下，盡可能保留雙邊濾波器的原始效果並提高輸出影像的品質，並實現於即時影像系統上。本研究致力實現於低運算複雜度、低硬體成本、影像品質高的雙邊濾波器演算法。

Image blurring is a technique majoring to reduce image noise and reduce detail when image processing and inferior sampling, Therefore, image blurring can effectively reduce image noise. This research focuses on a technology applied to image blurring, bilateral filters for hardware, and the desire to improve the image quality without consuming too much hardware cost and achieving weight reduction. Since exponential calculation is difficult to implement on hardware, we use the look-up table method as the basis for exponential calculation because of computing hardware and memory usage. By analyzing the two weights of the bilateral filter for the traditional bilateral filter, which is the Similarity weight and the Closeness weight. According to our algorithm, the original formula can be simplified to a certain extent to achieve the purpose of hardware simplification.
In addition to improving image quality, this study also focuses on reducing the computational complexity and hardware cost of the algorithm. the research also focused on reducing the computational complexity and hardware cost. We are devoted to implement the hardware of real-time applications with line buffer-based algorithm.

摘要 iii
ABSTRACT iv
目錄 iv
圖目錄 viii
表目錄 x
第一章 緒論1
1.1 背景簡介 1
1.2 研究動機與目標 2
1.3 論文架構 2
第二章 雙邊濾波器及其相關研究探討 3
2.1 影像平滑處理 3
2.1.1 相鄰像素平均法 3
2.1.2 中值濾波器 4
2.1.3 高斯分布平均法 4
2.1.4 雙邊濾波器 5
2.2 雙邊濾波器及其相關研究探討-基於查表法 6
2.2.1 基於查表法實現 6
2.3 雙邊濾波器及其相關研究探討-基於近似法 7
2.3.1 基於泰勒級數實現 7
2.3.2 基於CORDIC實現 8
2.4 雙邊濾波器及其相關研究探討-基於histogram 11
2.4.1基於積分直方圖實現 11
2.5 雙邊濾波器及其相關研究探討-基於histogram 12
2.5.1 基於查表法之優缺點 12
2.5.2 基於近似法之優缺點 12
2.5.3 基於積分直方圖實現之優缺點 12
2.5.4 低消耗且real-time的雙邊濾波器 12
第三章 基於查表法之雙邊濾波器硬體實現 16
3.1 雙邊濾波器解析 16
3.1.1 雙邊濾波器(Bilateral filter) 16
3.1.2 Spatial filter解析 18
3.1.3 Range filter解析 19
3.2 演算法流程與總節 25
3.2.1 演算法流程 25
3.2.2 演算法總結 25
第四章 硬體架構與設計 26
4.1 硬體架構介紹 26
4.2 硬體實作 28
4.2.1 spatial filter區塊 28
4.2.2 range filter區塊 29
4.2.3 convolution and normalize區塊 30
第五章 實驗結果與比較 31
5.1 實驗環境與驗證流程 32
5.2 影像品質評估方法 33
5.2.1 均方差(Mean Square Error, MSE) 33
5.2.2 峰值訊號雜訊比(Peak Signal to Noise Ratio, PSNR) 33
5.2.3 結構相似性指標(Structure Similarity, SSIM ) 34
5.2.4 影像評估方法流程 35
第六章 結論及未來展望 36
參考文獻. 37

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