4 4 2 RAW (PCA) RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( )
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- あゆみ こいたばし
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1 RAW 4 hakiyama@ok.ctrl.titech.ac.jp Abstract RAW RAW RAW RAW RAW 4 RAW RAW RAW 1 (CFA) CFA Bayer CFA [1] RAW CFA 1 2 [2, 3, 4, 5]. RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW RAW RAW [6, 7, 12] 3 [8, 9, 10, 11] RAW RAW RAW 4 G 2 4
![CFA [1] RAW CFA 1 2 [2, 3, 4, 5].](/docs-images/45/20574692/images/page_1.jpg "RAW RAW RAW RAW 3 [2, 3, 4, 5] (AWGN) [13, 14] RAW 2 RAW RAW")
2 4 4 2 RAW (PCA) RAW RAW [5] 4 RAW 4 Park [12] Park 2 RAW RAW 2 RAW y = Mx + n. (1) y RAW x RGB M CFA n.. R G B σr 2, σ2 G, σ2 B D n ( ) D m ( ) ˆx = D n (D m (Mx + n)). (2). η = D m (Mx + n) x. (3) ˆx = D m (D n (Mx + n)). (4) RAW RAW RAW RAW RAW 1 4 RAW 4 G R B GRBG RGGB BGGR GBRG 1 RAW
3 RAW RAW 4 4 RAW G 1, R, B, G 2 4 RGB Park RGB RGB 4 4 [12] RAW [7] 4 [ ] X = x 1 + n 1 x 2 + n 2 x m + n m, (5) Σ = 1 m 1 ( X µ 1 T m)( X µ 1 T m) T, (6) µ = 1 m m x i. (7) i=1 x i i 4 n i i m 4 1 m 1 m T Σ = Σ diag ( [ σ 2 Y 1 σ 2 Y 2 σ 2 Y 3 σ 2 Y 4 ] T ). (8) diag(z i ) z i σ 2 k 4 k GRBG
![3 Y 1 G 1 Y 2 Y 3 = P R B, (9) Y 4 G 2 P 11 P 12 P 13 P 14 P = P 21 P 22 P 23 P 24 P 31 P 32 P 33 P 34. (10) P 41 P 42 P 43 P 44 P Σ v k. [ ] T P = v 1 v 2 v 3 v 4. (11) P 4.](/docs-images/40/20574692/images/4-0.png "k σ 2 Y k = P 2 1kσ 2 G + P 2 2kσ 2 R + P 2 3kσ 2 B + P 2 4kσ 2 G. (12) 4. 3.3 4 4 RAW 4 RAW 4 4 1 (a) (b) (34.49dB). (35.28dB). 4 (σ = 20).")
4 3 Y 1 G 1 Y 2 Y 3 = P R B, (9) Y 4 G 2 P 11 P 12 P 13 P 14 P = P 21 P 22 P 23 P 24 P 31 P 32 P 33 P 34. (10) P 41 P 42 P 43 P 44 P Σ v k. [ ] T P = v 1 v 2 v 3 v 4. (11) P 4. k σ 2 Y k = P 2 1kσ 2 G + P 2 2kσ 2 R + P 2 3kσ 2 B + P 2 4kσ 2 G. (12) RAW 4 RAW (a) (b) (34.49dB). (35.28dB). 4 (σ = 20). RAW GRBG RGGB BGGR GBRG RAW 4 4 RAW RAW RAW RAW (PSNR)
5 1 24 RAW PSNR[dB] ( ) σ [14] to CFA [7] [12] proposed CPSNR [db] Proposed [12] [10] [11] [14]toCFA [9] [7] Noise level PSNR [db] Proposed [12] [14]toCFA [7] Noise sigma 5 PSNR. RAW RAW RAW CPSNR Kodak high resolution image dataset RAW RAW 0 RAW [14] [5] 4.1 RAW RAW BM3D [14] RAW PCASAD [7] Park [12] BM3D [14] RAW 6 CPSNR. RAW BM3D Park BM3D 1 5 Kodak dataset 24 PSNR PSNR 4.2 RAW CFA [5] LPAICI [9], JDDTV [10], LSLCD [11] RAW CPSNR CPSNR BM3D [14] RAW CPSNR Park [12] 0.5dB 7 Park BM3D
![97 CPSNR [db] 40 38 36 34 32 30 Proposed [12] [10] [11] [14]toCFA [9] [7] 28 5 10 15 20 25 30 35 40 Noise level PSNR [db] 50 45 40 35 30 Proposed [12] [14]toCFA [7] 25 0 10 20 30 40](/docs-images/45/20574692/images/page_5.jpg "Noise sigma 5 PSNR. RAW RAW RAW CPSNR Kodak high resolution image dataset 24 2048 3072 RAW RAW 0 RAW [14] [5] 4.1 RAW RAW BM3D [14] RAW PCASAD [7] Park [12] BM3D [14] RAW 6 CPSNR.")
![2 24 CPSNR[dB] ( ) Type Dm Dm Dn Joint Dn and Dm Dn Dm [5] σ [5] [5] + [14] [9] [10] [11] [14]toCFA [7] [12] proposed 5 34.84 38.76 38.48 38.99 39.01 39.48 39.13 39.07 39.52 10 29.35 35.38 35.81 36.](/docs-images/40/20574692/images/6-0.png "80 36.67 36.97 36.38 36.78 37.32 15 26.01 32.92 34.16 35.40 35.26 35.17 34.49 35.41 35.99 20 23.63 30.99 32.93 34.35 34.12 33.68 33.01 34.46 34.98 30 20.32 28.10 31.01 32.62 N/A 31.33 30.74 32.81 33.")
6 2 24 CPSNR[dB] ( ) Type Dm Dm Dn Joint Dn and Dm Dn Dm [5] σ [5] [5] + [14] [9] [10] [11] [14]toCFA [7] [12] proposed N/A N/A [11] σ = 20. (a) ground truth (b) [5] (c) [5]+[14] (d) [9] (e) [10] (f) [11] (g) [14]toCFA (h) [7] (i) [12] (j) proposed 7 Kodak high resolution image dataset (σ = 20). Park [12] 5 RAW RAW 4 RAW [1] B. Bayer, Color imaging array, U.S. Patent , [2] L. Zhang and X. Wu, Color demosaicking via directional linear minimum mean square-error estimation, IEEE Trans. on Image Processing, vol. 14, no. 12, pp , [3] X. Li, B. Gunturk, and L. Zhang, Image Demosaicing: A Systematic Survey, Proc. SPIE Electronic Imaging, vol. 6822, pp , [4] L. Zhang, X. Wu, A. Buades, and X. Li, Color demosaicking by local directional interpolation and nonlocal adaptive thresholding, Journal of Electronic Imaging, vol. 20, no. 2, pp , [5] D. Kiku, Y. Monno, M. Tanaka, and M. Okutomi, Residual Interpolation for Color Image Demosaicking, Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp , [6] A. Danielyan, M. Vehvilainen, A. Foi, V. Katkovnik, and K. Egiazarian Cross-color BM3D filtering of noisy raw data, in Proc. International Workshop on Local and Non-Local Approximation in Image Processing LNLA, pp.
![35 40 18.04 26.00 29.47 31.12 N/A 29.46 29.00 31.41 31.89 [11] σ = 20.](/docs-images/45/20574692/images/page_6.jpg "(a) ground truth (b) [5] (c) [5]+[14] (d) [9] (e) [10] (f) [11] (g) [14]toCFA (h) [7] (i) [12] (j) proposed 7 Kodak high resolution image dataset (σ = 20). Park [12] 5 RAW RAW 4 RAW [1] B.")
7 , [7] L. Zhang, R. Lukac, X. Wu, and D. Zhang, PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras, IEEE Trans. on Image Processing, vol. 18, no. 4, pp , [8] K. Hirakawa, and T.W. Parks, Joint demosaicing and denoising Image Processing, IEEE Trans. on Image Processing, vol. 15, no. 8, pp , [9] D. Paliy, V. Katkovnik, R. Bilcu, S. Alenius, and K. Egiazarian, Spatially Adaptive Color Filter Array Interpolation for Noiseless and Noisy Data, Int. J. Imaging Sys. Tech., Sp. Iss. Appl. Color Image Process., vol. 17, no. 3, pp , [10] L. Condat, and S. Mosaddegh, Joint demosaicking and denoising by total variation minimization, Proc. of IEEE Int. Conf. on Image Processing (ICIP), pp , [11] E. Dubois, and G. Jeon, Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-Chroma Demultiplexing and Noise Level Estimation, IEEE Trans. on Image Processing, vol. 22, no. 1, pp , [12] S. H. Park, H. S. Kim, S. Lansel, M. Parmar, and B. A. Wandell, A case for denoising before demosaicking color filter array data Asilomar Conf. on Signals, Systems, and Computers, pp , [13] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration. IEEE Int. Conf. on Computer Vision (ICCV), pp , [14] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image denoising with block-matching and 3D filtering, Proc. SPIE Electronic Imaging, no. 6064A 30, 2006.
IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt
![IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt](/thumbs/104/162109798.jpg "IPSJ SIG Technical Report Vol.2012-CVIM-182 No /5/ RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filt")
1 1 1 1 1. 4 3 RGB [1], [2], [3], [4], [5] [6], [7], [8], [9] 1 (MSFA: Multi-Spectrum Filter Array) 1 [8], [9] RGB 1 Tokyo Institute of Technology 1 [10], [11], [12], [13], [14] [15] Parmar Wiener RGB
16 June 2004 2 RAW Fig. 2 High resolution color image reconstruction from RAW data. u(i 1,i 2 )= p(i 1 x, i 2 y)i(x, y)dxdy (1) 1 demosaicking Fig. 1
Vol. 45 No. SIG 8(CVIM 9) June 2004 RAW, RAW demosaicking High Resolution Color Image Reconstruction Using Raw Data of a Single Imaging Chip Tomomasa Gotoh, and Masatoshi Okutomi The limited resolution
h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5
![h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5](/thumbs/91/106545489.jpg "h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)] (5 3) S (ω) s(n) S (ω) = F[s(n)] (5")
1 -- 5 5 2011 2 1940 N. Wiener FFT 5-1 5-2 Norbert Wiener 1894 1912 MIT c 2011 1/(12) 1 -- 5 -- 5 5--1 2008 3 h(n) x(n) s(n) S (ω) = H(ω)X(ω) (5 1) H(ω) H(ω) = F[h(n)] (5 2) F X(ω) x(n) X(ω) = F[x(n)]
204/3 Vol. J97 A o. 3 HDR HDR wavelet-shrinkage wavelet wavelet wavelet-shrinkage wavelet RGB wavelet wavelet wavelet HDR wavelet 2.
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1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +
![1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +](/thumbs/81/83701592.jpg "1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +")
3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
![2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server](/thumbs/91/107223763.jpg "2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server")
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2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2
![2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2](/thumbs/42/22941906.jpg "2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2")
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E-1E-9 1 5) 15) ) 1 E-1 E-5 E-1E-5 11) 14 (E-2)(E-3) E-1E-5 Exif E-6E-9 E-6 2.1, 2.2 E E-1 E-5 E-6E-9 1-1) 1 Exif 11) (E-1) (E-2E-5) E-7
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(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s
![(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s](/thumbs/75/72066128.jpg "(a) 1 (b) 3. Gilbert Pernicka[2] Treibitz Schechner[3] Narasimhan [4] Kim [5] Nayar [6] [7][8][9] 2. X X X [10] [11] L L t L s L = L t + L s")
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A Technical Survey of Process Control Systems 3 Yutaka Wakasa Abstract Yutaka Wakasa 95 1 2 96 Vol.11 2008.March 2.1 2.2 97 98 Vol.11 2008.March 99 3 3.1 3.2 3.3 100 Vol.11 2008.March 101 3.4 102 Vol.11
25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52
26 1 11M15133 25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52 1 2 2 4 2.1.............................. 4 2.2.................................. 5 2.2.1...........................
2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [
![2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [](/thumbs/93/111684107.jpg "2.2 (a) = 1, M = 9, p i 1 = p i = p i+1 = 0 (b) = 1, M = 9, p i 1 = 0, p i = 1, p i+1 = 1 1: M 2 M 2 w i [j] w i [j] = 1 j= w i w i = (w i [ ],, w i [")
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H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)
![H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)](/thumbs/92/108069141.jpg "H(ω) = ( G H (ω)g(ω) ) 1 G H (ω) (6) 2 H 11 (ω) H 1N (ω) H(ω)= (2) H M1 (ω) H MN (ω) [ X(ω)= X 1 (ω) X 2 (ω) X N (ω) ] T (3)")
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スライド 1
swk(at)ic.is.tohoku.ac.jp 2 Outline 3 ? 4 S/N CCD 5 Q Q V 6 CMOS 1 7 1 2 N 1 2 N 8 CCD: CMOS: 9 : / 10 A-D A D C A D C A D C A D C A D C A D C ADC 11 A-D ADC ADC ADC ADC ADC ADC ADC ADC ADC A-D 12 ADC
スライド 1
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Application of image correlation technique to determination of in-plane deformation distribution of paper Toshiharu Enomae Graduate School of Agricultural and Life Sciences The University of Tokyo 1 Peters
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196 MEDICAL IMAGING TECHNOLOGY Vol. 36 No. 4 September 2018 MRI 1 2 2 1 3 4 1 MRI MRI ScSR MRI T1 T2 FLAIR TOF ScSR PSNR SSIM T1 T1 T2 FLAIR ScSR PSNR SSIM Bilinear Bicubic Lanczos p
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1611 原著 論文受付 2009 年 6 月 2 日 論文受理 2009 年 9 月 18 日 Code No. 733 ピクセル開口率の向上による医用画像表示用カラー液晶モニタの物理特性の変化 澤田道人 石川晃則 1) 松永沙代子 1) 1) 石川陽子 有限会社ムツダ商会 1) 安城更生病院放射線技術科 緒言 3D PET/CT Fusion 1 liquid crystal display:
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平成 28 年 6 月 3 日 報道機関各位 東京工業大学広報センター長 岡田 清 カラー画像と近赤外線画像を同時に撮影可能なイメージングシステムを開発 - 次世代画像センシングに向けオリンパスと共同開発 - 要点 可視光と近赤外光を同時に撮像可能な撮像素子の開発 撮像データをリアルタイムで処理する
平成 28 年 6 月 3 日 報道機関各位 東京工業大学広報センター長 岡田 清 カラー画像と近赤外線画像を同時に撮影可能なイメージングシステムを開発 - 次世代画像センシングに向けオリンパスと共同開発 - 要点 可視光と近赤外光を同時に撮像可能な撮像素子の開発 撮像データをリアルタイムで処理する画像処理システムの開発 カラー画像と近赤外線画像を同時に撮影可能なプロトタイプシステムの開発 概要 国立大学法人東京工業大学工学院システム制御系の奥富正敏教授らと