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研究生:王柏東
研究生(外文):Po-Tong Wang
論文名稱:基於卷積神經網路與 U 型卷積網路之影像感測器的色彩特性化建模
論文名稱(外文):Colorimetric Characterization of Image Sensors Based on CNN and U-Net Modeling
指導教授:周瑞仁周瑞仁引用關係
指導教授(外文):Jui Jen Chou
口試委員:陳倩瑜顏炳郎孫沛立羅梅君
口試委員(外文):Chien-Yu ChenPing-Lang YenPei-Li SunMei-Chun Lo
口試日期:2021-01-29
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:生物機電工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:58
中文關鍵詞:彩色影像感測器卷積神經網路 (CNN)U 型卷積網路 (U-Net)色彩特性化逐像素迴歸資料擴增
外文關鍵詞:color image sensorconvolutional neural network (CNN)U-Net convolutional network (U-Net)pixel-wise regressioncolorimetric characterizationdata augmentation
DOI:10.6342/NTU202100640
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本研究以卷積神經網路 (convolutional neural network, CNN) 與 U 型卷積網路 (U-Net convolutional network, U-Net) 演算法為核心技術, 並根據 CIE ( 法語:Commission Internationale de l´Eclairage,國際照明委員會 ) 所推薦基於人眼色彩視覺為基礎的色度測定標準,進行色彩特性化建模 (colorimetric characterization modeling),以實現彩色影像感測器 (color image sensor) 之高精度色彩特性化。

影像感測器之色彩特性化是一項艱鉅的任務。首先彩色影像感測器所感測的 RGB 訊號不能當作色彩度量 (colorimetry),因為相同的圖像以不同的影像感測裝置量測所產生的 RGB 訊號差異很大,同樣的 RGB 感測訊號可能代表不同的顏色,因此 RGB 訊號不是 CIE 所規範色彩度量的標準 ( 例如 CIELAB 或 CIE XYZ)。所謂影像感測器之色彩特性化係透過演算法進行 RGB 與 CIELAB/CIE XYZ 的色彩空間轉換。過去的研究主要採用包含對照表內插模式 (LUT-interpolation model)、迴歸模式 (regression model) 與類神經網路模式 (artificial neural network model) 等方法,到目前為止,色彩特性化的技術經過評測結果:還無法達到接近分光光譜儀的測色水準,其中主要原因為色彩特性化係一非線性的複雜關係,因此,色彩特性化的演算法還有很大的進步空間。

基於 CNN 卷積神經網路為基礎,本研究試圖突破傳統 (3 x N) 多項式迴歸建模的度量精度。對於 CNN 色彩特性化技術的研究,我們透過影像感測器自動擷取 IT8.7/4 色彩導表,將 (3 x 8 x 8 ) 像素 ( 3 為 RGB 三顏色, (8 x 8) 為像素大小 ) 輸入 CNN 卷積神經網路,再映射由分光光譜儀量測所輸出的 CIELAB (3 x 1 x 1) 像素 ( 3 為 LAB 三顏色 , (1 x 1) 為像素 ) 數據,經過 5 次迭代的卷積神經網路學習,到第 5 次迭代卷積層已擴增為 8 幅 (3 x 8 x 32) 特徵圖 (feature map),最後平面化 (flatten) 生成 6,144 筆色彩特徵向量輸入至倒傳遞神經網路(back-propagation neural network, BP NN)。 在色彩特性化平均色差值的評比:CNN 建模的 ΔE*ab 為 0.48 優於傳統 (3 x 11) 多項式迴歸建模的 ΔE*ab 為 3.03。

CNN 色彩特性化建模面臨的挑戰:CNN 訓練所需的電腦運算量龐大、訓練時間長、訓練的色彩數據不足、與可驗證的色彩數據過少等。為了克服上述瓶頸, 本研究藉由 U-Net 突破 CNN 訓練運算時間的問題:U-Net 只花了 1,000 波期 (epoch) 的學習週期而 CNN 需要耗費 100,000 波期的學習週期。透過 U-Net 學習可以解決 IT8.7/4 色彩導表數據不足的問題:U-Net 僅從 ISO 12640 (CIELAB/ SCID) 的六幅影像,再利用資料擴增 (data augmentation) 技術標註 32,027,200 色面:U-Net 驗證 ISO 12640 (CIELAB/SCID) 的兩幅 CIELAB 影像中 9,338,456 像素與 1,626,192 顏色;而 CNN 從 IT8.7/4 色彩導表中驗證 39,488 像素與 317 顏色。

本研究利用 CNN 與 U-Net 卷積網路所建構之創新色彩特性化方法,相較於傳統 (3 × N) 多項式迴歸建模的性能表現更勝一籌,經由研究結果驗證 CNN 卷積神經網路的平均色差值 ΔE*ab 為 0.48,而 U-Net 的平均色差值 ΔE*ab 為 0.52,二者皆優於傳統 (3 x 11) 多項式迴歸模式的 ΔE*ab 為 3.03。雖然 U-Net 建模的平均色差值的精準度略遜於 CNN 模型,但是 U-Net 建模的運算效率比 CNN 建模快約六倍,實驗透過配備 Nvidia GPU GTX 1080 Ti 的 PC,驗證一張 ISO 12640 (CIELAB/ SCID) 彩色影像之特性化模型,CNN 模型運算平均需要 5 秒,而 U-Net 模型運算平均需要 0.8 秒。本研究證實藉由 CNN 與 U-Net 所產出的色彩特性化建模演算法技術,可提升影像感測器裝置之色彩特性化更高的精度。
In this study, the colorimetric characterization of a color image sensor was developed and modeled using a convolutional neural network (CNN) and an U-Net convolutional network (U-Net), in according with the International Commission on Illumination (CIE), which is the recommended colorimetric measurement standard based on human eye color vision as the basis. Color image sensors can be incorporated into compact devices to detect the color of objects under a wide range of light sources and brightness. They should be colorimetrically characterized to realize the high-precision color characterization of the innovative color image sensor technology.

However, the colorimetric characterization of image sensors is a difficult task. First, the red, green, and blue (RGB) signals are not colorimetrically captured by the color image sensor. They are device-dependent, which means that different sensing devices generate different spectrum responses for the same RGB signal. Therefore, the generated RGB signals do not adhere to the colorimetry standards regulated by CIE, such as CIELAB and CIE XYZ. The colorimetric characterization of an image sensor applies algorithms to transform RGB to CIELAB/CIE XYZ color space. The previously used methods for this purpose mainly include the look up table (LUT) interpolation model, regression model, and artificial neural network model. The colorimetric characterization technology evaluated thus far has not reached a colorimetric measurement level close to that of a spectrometer. This is mainly because the system used for colorimetric characterization is nonlinear and complex, and thus, needs to be considerably improved.

To address this issue, this study aimed to apply a CNN colorimetric characterization algorithm, which is superior to the traditional 3 × N polynomial regression modeling. Here, the color patch of the IT8.7/4 target is automatically captured using the image sensor and converted into (3 × 8 × 8) pixels, where 3 represents the RGB colors and 8 × 8 is the pixel size input to the CNN. Then, the image is mapped to CIELAB (3 × 1 × 1) pixels, where 3 indicates the three Lab colors and 1 × 1 is the pixel size that represents the data measured and output by the spectrometer. After five iterations of CNN learning, the convolutional layer expands to eight feature maps, each of (3 × 8 × 32) pixels, and finally flattens to generate 6,144 color feature vectors, which are input to the back-propagation neural network (BP NN). The average color difference value ( ΔE*ab ) for CNN modeling is 0.48, which is better than that obtained for the traditional 3 × 11 polynomial regression modeling (3.03).

CNN colorimetric characterization modeling faces the following challenges: CNN training requires considerable computation, long training time, so far training color data, and negligible verifiable color data. To overcome the aforementioned issues, this study used U-Net. First, U-Net requires only 1000 epochs of a learning cycle, whereas CNN requires 100 000 epochs. Thus, the issue of excessive training time required for CNN computation can be resolved, Second, U-Net can overcome the issue of insufficient data in the IT8.7/4 target: it uses the data augmentation technology to label 32 027 200 color patches over 256 × 256 pixels, resulting in 2098 billion pixels, including 6 885 222 colors for only six ISO 12640 (CIELAB/ SCID) images; by contrast, CNN can only randomly select 1000 colors from the 1617 color patches in the IT8.7/4 target, which amounts to only 1000 patch colors over 64000 pixels. Moreover, U-Net validates 9 338 456 pixels and 1 626 192 colors in two CIELAB images of ISO 12640 (CIELAB/SCID), whereas CNN validates 39 488 pixels and 617 colors from the IT8.7/4 target.

The innovative colorimetric characterization methods used in this study to apply CNN or U-Net, which are superior to the traditional 3 × N polynomial regression modeling. The study results validate the CNN performance. The ΔE*ab values of CNN and U-Net are 0.48 and 0.52, respectively, both of which are superior to the values achieved by the traditional 3 × 11 polynomial regression model (3.03). Although the accuracy of the ΔE*ab value modeled by U-Net is slightly inferior to that modeled by CNN, the computation efficiency of U-Net is approximately six times faster than that of CNN. The experimental results are validated using a PC equipped with Nvidia GPU GTX 1080 Ti. Using ISO 12 640 (CIELAB/SCID) standard color images for the colorimetric characteristic model, the application of the CNN model requires an average of 5 s, whereas the U-Net model requires 0.8 s. Thus, the colorimetric characterization modeling algorithm technology implemented using CNN and U-Net exhibit improved colorimetric characterization accuracy of the image sensor device.
目錄
口試委員審定書 i
誌謝 ii
摘要 iv
Abstract vi
目錄 ix
圖目錄 xi
表目錄 xiii
第 1 章 前言 1
第 2 章 文獻探討 3
2.1. 色彩特性化介紹 3
2.1.1. 影像感測器的色彩特性化設備與材料 3
2.1.2 色彩特性化演算法 5
2.2 類神經網路的演算法 7
2.3 深度學習的演算法 11
2.3.1 CNN演算法 11
2.3.2 U-Net 演算法 13
第 3 章. 材料與方法 19
3.1 傳統多項式迴歸之色彩特性化建模 19
3.2 CNN 色彩特性化建模 20
3.2.1 CNN 色彩特性化的材料 20
3.2.2 CNN 色彩特性化訓練與驗證的數據 22
3.2.3 CNN 色彩特性化的方法 23
3.3 U-Net 色彩特性化建模 27
3.3.1 U-Net 色彩特性化的材料 28
3.3.2 U-Net 色彩特性化訓練與驗證的數據 29
3.3.3 U-Net 色彩特性化的方法 32
第 4 章 結果與討論 40
4.1 CNN 色彩特性化的結果 40
4.2 U-Net 色彩特性化的結果 45
4.3 討論 50
第 5 章. 結論 52
參考文獻 54

圖目錄
圖 2-1 IT8.7/4 色彩導表 5
圖 2-2 非監督式自組織映射類神經網路架構圖 8
圖 2-3 監督式倒傳遞類神經網路架構圖 9
圖 2-4 複合式類神經網路架構圖 10
圖 2-5 LeNet-5 的模型架構圖 12
圖 2-6 U-Net 架構圖 13
圖 2-7 藉由變形影像為資料擴增技術 14
圖 2-8 (2 x 2) 卷積核進行最大池化 15
圖 2-9 U-Net 編碼與解碼架構圖 18
圖 2-10 U-Net 編碼與解碼融合之運作示意 18
圖 3-1 多項式迴歸之色彩特性化實施流程圖 19
圖 3-2 多項式迴歸的「點 - 對 - 點」之色彩空間轉換示意 20
圖 3-3 色彩特性化實施流程圖 21
圖 3-4 CNN 「色塊影像 - 對 - 點」之色彩空間轉換示意圖 22
圖 3-5 CNN 色彩特性化架構 23
圖 3-6 Sigmoid 、Tanh 與 ReLU 激勵函數 25
圖 3-7 ReLU 激勵函數使網路變得更為稀疏 26
圖 3-8 汰減 (dropout) 以刪減鍵結的神經元示意圖 27
圖 3-9 U-Net 實現「影像 - 對 - 影像 」之逐像素色彩空間轉換示意圖 28
圖 3-10 ISO 12640(CIELAB/SCID)印前技術-數位交換資料之彩色影像 28
圖 3-11 U-Net 色彩特性化實施流程圖 29
圖 3-12 ISO 12640 (CIELAB/SCID) 標準圖庫中 N2 - N7 為訓練的圖檔 30
圖 3-13 輸入的 RGB 訓練影像流程圖 31
圖 3-14 ISO 12640 (CIELAB/SCID) 標準圖庫中 N1與 N8 為驗證的圖檔 31
圖 3-15 U-Net 色彩特性化架構 32
圖 3-16 自動編碼器之架構 33
圖 3-17 U-Net 新增級聯以改良自動編碼器的架構 34
圖 3-18 U-Net 級聯運作示意圖 34
圖 3-19 U-Net 之 (1 x 1) 卷積核之運作 3D 示意圖 35
圖 3-20 U-Net 的收縮路徑暨卷積核之對照圖 36
圖 3-21 U-Net 的 (1 x 1) 卷積核 37
圖 3-22 U-Net 的擴張路徑暨卷積核之對照圖 38
圖 4-1 CNN 色彩特性化建模之色差直方圖 42
圖 4-2 多項式 (3 x 11) 迴歸色彩特性化建模之色差直方圖 42
圖 4-3. CNN色彩特性化建模 ΔE*ab >6 之 4 筆顏色比較圖 43
圖 4-4 ISO 12640 (CIELAB/SCID) 標準圖 N8_LAB 44
圖 4-5 CNN 色彩特性化建模之推論圖 N8CNN_LAB 44
圖 4-6 多項式 (3 x 11) 迴歸建模之推論圖 N83 x 11_LAB 45
圖 4-7 U-Net 色彩特性化建模的 MSE 與 val_MSE 學習曲線 46
圖 4-8 U-Net 色彩特性化建模的 MAE 與 val_MAE 學習曲線 46
圖 4-9 U-Net 色彩特性化建模於 148 波期已漸收斂 47
圖 4-10 U-Net 建模建模影像 N1_LAB 可視化比較圖 47
圖 4-11 U-Net 建模建模影像 N1_LAB 色差值: ΔE*ab ≤ 1 佔比 99.79% 48
圖 4-12 U-Net 建模建模影像 N8_LAB 可視化比較圖 48
圖 4-13 U-Net 建模建模影像 N8_LAB 色差值 ΔE*ab ≤ 1 佔比 93.24% 49

表目錄
表 4.1 CNN色彩特性化建模 ΔE*ab >6 之 4 筆顏色對照表. 43
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