Vol1-CVIM-172 No.7 21/5/ Shan 1) 2 2)3) Yuan 4) Ancuti 5) Agrawal 6) 2.4 Ben-Ezra 7)8) Raskar 9) Image domain Blur image l PSF b / = F(
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1 Vol1-CVIM-172 No.7 21/5/27 1 Proposal on Ringing Detector for Image Restoration Chika Inoshita, Yasuhiro Mukaigawa and Yasushi Yagi 1 A lot of methods have been proposed for restoring blurred images due to motion of the camera or subjects. The major problem of the restoration process is that the deblurred images include wave-like artifacts called ringing. In this paper, we propose a ringing detector for distinguish the artifact from some textures included in natural images. To design the ringing detector, we focused attention on the fact that the ringings are caused by null frequency of the point spread function. Ringings are detected by evaluating whether the deblurred image includes sine waves corresponding the null frequencies across the entire image with uniform phase. By combining the ringing detector with a debluring process, we can reduce ringing artifacts in the restores images. We demonstrate the effectiveness of the proposed ringing detector by some experiments using synthetic images and real images Shan 1) Yuan 2) PSF 1 Insutitute of Scientific and Industrial Research, Osaka University 1 c 21 Information Processing Society of Japan
2 Vol1-CVIM-172 No.7 21/5/ Shan 1) 2 2)3) Yuan 4) Ancuti 5) Agrawal 6) 2.4 Ben-Ezra 7)8) Raskar 9) Image domain Blur image l PSF b / = F(l) F(b) F(f) Frequency domain 1 3. Inverse Deblurred image ( PSF ) l = f b (1) f b PSF l PSF (1) (1) F (l) = F ( f )F (b) (2) F (g) g (2) PSF ( ) F (l) r = F 1 F (b) F 1 (g) g r PSF (3) 1 (3) 2 c 21 Information Processing Society of Japan
3 Vol1-CVIM-172 No.7 21/5/27 (a) (b) (a) (b) PSF (2) 3 1 PSF 2 (3) 4 PSF PSF ( ) r(x, y) = f (x, y) + e i (x, y) (4) i r(x, y) f (x, y) e i (x, y) (5) e i (x, y) = k i cos(2π(a i x + b i y) + c i ) (5) a i x b i y PSF k i c i 2 (4) (a) 2 4(b) (a) (b) 3 c 21 Information Processing Society of Japan
4 PSF (b) 1 (b) 2 ( 1 ) ( 2 ) (1) (6) (7) g(x, y) = r(x s, y t)w(s, t) (6) s,t w(x, y) = 1 x 2 +y 2 2πd 2 e 2d 2 cos(2π(ax + by)) (7) d a x b y g(x, y) 5 1 (2) e i (x, y) c i k i k i 1 C i (c) c C i (c) = error wave error wave * (a) * (b) 5 (g i (x, y) g i (x, y))(e i (x, y, c) e i (x, y, c)) x,y (8) (g i (x, y) g i (x, y)) 2 (e i (x, y, c) e i (x, y, c)) 2 x,y x,y g i (x, y) = r(x s, y t)w i (s, t) (9) s,t w i (x, y) = 1 x 2 +y 2 2πd 2 e 2d 2 cos(2π(a i x + b i y)) (1) e i (x, y, c) = cos(a i x + b i y + c) (11) g i (x, y) g i (x, y) = (12) N x,y e i (x, y, c) e i (x, y, c) = (13) N i,x,y N Vol1-CVIM-172 No.7 21/5/27 C i (c) (b) 4 c 21 Information Processing Society of Japan
5 Vol1-CVIM-172 No.7 21/5/27 1. Image restoration in frequency domain 2. Search Image domain non-invertible frequency 3. Apply ringing detector 3 25 Blur image l(known) PSF b(known) Deblurred image Inverse F(b) Small value Deblurred image Large value s e 2 tim f o r 15 e b m u 1 n 5 / = 4. Minimize ringing artifact output of ringing detector F(l) Frequency domain F(b) 7 (a=25,b=25) Deblurred image Refined image PSF PSF 6 1 (3) 2 PSF c i k i 4-1 e i (x, y) c i (8) c 4-2 k i k i 4-3 c i k i 2 4 PSF 5. 5 (1) x y a i, b i,,25,,5 x y 1 frickr( 6 scene, flower, animal, building, human 7 x y c 21 Information Processing Society of Japan
6 Vol1-CVIM-172 No.7 21/5/27 1 x-axis frequency a y-axis frequency b (a) PSF1 (b) PSF1 (c) PSF1 (a) building (b) desk (c) road (d) PSF2 (e) PSF2 (f) PSF2 8 9 PSF 5 PSF PSF (1) 8 9 PSF 9(c) 9(f) PSF PSNR. RL 1) 11) 2 3 PSNR PSF1 PSF2 PSNR 1 building PSF1 11 PSF2 PSF1 PSF2 2 PSF1 PSNR[dB] RL building desk road PSF2 PSNR[dB] RL building desk road RL 6 c 21 Information Processing Society of Japan
7 Vol1-CVIM-172 No.7 21/5/27 (a) (b) (c) RL (d) 1 PSF1 (a) (b) (a) (b) (c) RL (d) 11 (a) PSF2 12 PSF1 (b) PSF1 PSF2 12 PSF1 ( ) 1(a) PSF 5.3 PSF PSF PSF PSF 15 PSF PSF PSF 7 c 21 Information Processing Society of Japan
8 (a) (b) (c) PSF PSF 5 PSF PSF PSF PSF 5 PSF2 MATLAB 1 6. PSF PSF PSF PSF Vol1-CVIM-172 No.7 21/5/27 1) Q.Shan, J.Jia, and A.Agarwala, High-quality motion deblurring from a single image, ACM Transactions on Graphics, Vol. 27, No. 3, Article 73, 28. 2) L.Yuan, J.Sun, L.Quan, and H.-Y.Shum, Progressive Inter-scale and Intra-scale Non-blind Image Deconvolution, ACM Transactions on Graphics, Vol. 27, No. 3, Article 74, 28. 3),,,, D, Vol. J92-D, No. 8, pp , 28. 4) L.Yuan, J.Sun, L.Quan, and H.-Y.Shum, Image Deblurring with Blurred/Noisy Image Pairs, ACM Transactions on Graphics, Vol. 26, No. 3, Article 1, 27. 5) C.Ancuti, C.O.Ancuti, and P.Bekaert, Deblurring by Matching, EUROGRAPHICS, Vol. 28, No. 2, 29. 6) A.Agrawal, Y.Xu, and R.Raskar, Invertible Motion Blur In Video, ACM Transactions on Graphics, Vol. 28, Issue 3, 29 7) M.Ben-Ezra, and S.K.Nayar, Motion Deblurring using Hybrid Imaging, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-8, 23. 8) M.Ben-Ezra, and S.K.Nayar, Motion-Based Motion Deblurring, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 6, 24. 9) R.Raskar, A.Agrawal, and J.Tumblin, Coded Exposure Photography: Motion Deblurring using Fluttered Shutter, ACM Transactions on Graphics, Vol. 25, Issue 3, 26 1) W.H.Richardson, Bayesian-Based Iterative Method of Image Restoration, Journal of Optical Society of America, Vol. 62, No. 1, ) N.Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MIT Press, c 21 Information Processing Society of Japan
(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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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 630-0192 8916-5 E-mail: {kaduya-o,takafumi-t,goshiro,uranishi,miyazaki,kato}@is.naist.jp,.,,.,,,.,,., CG.,,,
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5-3 Photonic Antennas and its Application to Radio-over-Fiber Wireless Communication Systems LI Keren, MATSUI Toshiaki, and IZUTSU Masayuki In this paper, we presented our recent works on development of
[2] OCR [3], [4] [5] [6] [4], [7] [8], [9] 1 [10] Fig. 1 Current arrangement and size of ruby. 2 Fig. 2 Typography combined with printing
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1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
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制御理論を用いた在庫管理モデルの一解析 * リードタイムが変動する場合 西平直史 1 [1, 2, 3, 4] [1] [2, 3, 4] 1 1 3 2 [2] = +w(k) d(k) (1) 2014 12 1 1 制御理論を用いた在庫管理モデルの一解析 西平 k w(k) d(k) L k u(k) (2) (1) 2 w(k) =u(k L) (2) = +u(k L) d(k) (3)
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Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [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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す 局所領域 ωk において 線形変換に用いる係数 (ak 画素の係数 (ak bk ) を算出し 入力画像の信号成分を bk ) は次式のコスト関数 E を最小化するように最適化 有さない画素に対して 式 (2) より画素値を算出する される これにより 低解像度な画像から補間によるアップサ E(
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[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
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IEEE802.11 [1]Bluetooth [2] 1 1 (1) [6] Ack (Ack) BEC FEC (BEC) BEC FEC 100 20 BEC FEC 6.19% 14.1% High Throughput and Highly Reliable Transmission in MANET Masaaki Kosugi 1 and Hiroaki Higaki 1 1. LAN
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25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA
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Feature Article Imaging of minuscule amounts of chemicals, Scannimg Chemical Microscope --- Increasing analysis information through imaging --- Abstract We have developed a Scanning Chemical Microscope
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25 Feature Selection for Prediction of Stock Price Time Series 1140357 2014 2 28 ..,,,,. 2013 1 1 12 31, ( ) 3.,., 3.,., 500, 233.,, 3,,.,, i Abstract Feature Selection for Prediction of Stock Price Time
Silhouette on Image Object Silhouette on Images Object 1 Fig. 1 Visual cone Fig. 2 2 Volume intersection method Fig. 3 3 Background subtraction Fig. 4
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2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1,
1 1 2,,.,.,,, SIFT.,,. Pitching Motion Analysis Using Image Processing Shinya Kasahara, 1 Issei Fujishiro 1 and Yoshio Ohno 2 At present, analysis of pitching motion from baseball videos is timeconsuming
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1 2 4 5 9 10 12 3 6 11 13 14 0 8 7 15 Iteration 0 Iteration 1 1 Iteration 2 Iteration 3 N N N! N 1 MOPT(Merge Optimization) 3) MOPT 8192 2 16384 5 MOP
10000 SFMOPT / / MOPT(Merge OPTimization) MOPT FMOPT(Fast MOPT) FMOPT SFMOPT(Subgrouping FMOPT) SFMOPT 2 8192 31 The Proposal and Evaluation of SFMOPT, a Task Mapping Method for 10000 Tasks Haruka Asano
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22 Google Trends Estimation of Stock Dealing Timing using Google Trends
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Partial Copy Detection of Line Drawings from a Large-Scale Database Weihan Sun, Koichi Kise Graduate School of Engineering, Osaka Prefecture University E-mail: sunweihan@m.cs.osakafu-u.ac.jp, kise@cs.osakafu-u.ac.jp
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Forecasting Model for Electricity Consumption in Residential House Based on Time Series Analysis * ** *** Shuhei Kondo Nobayasi Masamori Shuichi Hokoi ( 2015 7 3 2015 12 11 ) After the experience of electric
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Title < 論文 > 公立学校における在日韓国 朝鮮人教育の位置に関する社会学的考察 : 大阪と京都における 民族学級 の事例から Author(s) 金, 兌恩 Citation 京都社会学年報 : KJS = Kyoto journal of so 14: 21-41 Issue Date 2006-12-25 URL http://hdl.handle.net/2433/192679 Right
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Present Situation and Problems on Aseismic Design of Pile Foundation By H. Hokugo, F. Ohsugi, A. Omika, S. Nomura, Y. Fukuda Concrete Journal, Vol. 29, No. 8, pp. 4-12, Aug. 1986 Synopsis The pile foundation