[1] BAYER B E.Color imaging array:US3971065[P].1976-07-20. [2] KHASHABI D,NOWOZIN S,JANCSARY J,et al.Joint demosaicing and denoising via learned nonparametric random fields[J].IEEE Transactions on Image Processing,2014,23(12):4968-4981. [3] NI K S,NGUYEN T Q.An adaptable k-nearest neighbors algorithm for MMSE image interpolation[J].IEEE Transactions on Image Processing,2009,18(9):1976-1987. [4] GRIBBON K T,BAILEY D G.A novel approach to real-time bilinear interpolation[C]//Proceedings of the 2nd IEEE International Workshop on Electronic Design,Test and Applications.Washington D.C.,USA:IEEE Press,2004:126-131. [5] ADAMS J E,HAMILTON J F.Adaptive color plane interpolation in single sensor color electronic camera:US5652621[P].1997-07-29. [6] MUKHERJEE J,MOORE M S,MITRA S K.Color demosaicing with constrained buffering[C]//Proceedings of the 6st International Symposium on Signal Processing and Its Applications.Washington D.C.,USA:IEEE Press,2001:52-55. [7] ZHANG L,WU X L.Color demosaicking via directional linear minimum mean square-error estimation[J].IEEE Transactions on Image Processing,2005,14(12):2167-2178. [8] CHUNG K H,CHAN Y H.Color demosaicing using variance of color differences[J].IEEE Transactions on Image Processing,2006,15(10):2944-2955. [9] ZHANG L,WU X,BUADES A,et al.Color demosaicking by local directional interpolation and nonlocal adaptive thresholding[J].Journal of Electronic Imaging,2011,20(2):1-16. [10] KIKU D,MONNO Y,TANAKA M,et al.Minimized-Laplacian residual interpolation for color image demosaicking[C]//Proceedings of SPIE'14.San Francisco,USA:[s.n.],2014:1-8. [11] MONNO Y,KIKU D,TANAKA M,et al.Adaptive residual interpolation for color image demosaicking[C]//Proceedings of 2015 IEEE International Conference on Image Processing.Washington D.C.,USA:IEEE Press,2015:3861-3865. [12] KIKU D,MONNO Y,TANAKA M,et al.Beyond color difference:residual interpolation for color image demosaicking[J].IEEE Transactions on Image Processing,2016,25(3):1288-1300. [13] ZHANG L,ZHANG D.A joint demosaicking-zooming scheme for single chip digital color cameras[J].Computer Vision and Image Understanding,2007,107(1/2):14-25. [14] 刘丹华,李平,高大化,等.基于压缩感知的正六边形CFA模式彩色图像去马赛克方法[J].光电子激光,2015,26(2):360-367. LIU D H,LI P,GAO D H,et al.Color demosaicking method based on regular hexagon CFA pattern and compressive sensing[J].Journal of Optoelectronics Laser,2015,26(2):360-367.(in Chinese) [15] LLAMAS J,LERONES P M,MEDINA R,et al.Classification of architectural heritage images using deep learning techniques[J].Applied Sciences,2017,7(10):992-1016. [16] 厉智,孙玉宝,王枫,等.基于深度卷积神经网络的服装图像分类检索算法[J].计算机工程,2016,42(11):309-315. LI Z,SUN Y B,WANG F,et al.Clothing image classification and retrieval algorithm based on deep convolutional neural network[J].Computer Engineering,2016,42(11):309-315.(in Chinese) [17] HOU Y H,LI Z Y,WANG P C,et al.Skeleton optical spectra-based action recognition using convolutional neural networks[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,28(3):807-811. [18] 曹晋其,蒋兴浩,孙锬锋.基于训练图CNN特征的视频人体动作识别算法[J].计算机工程,2017,43(11):234-238. CAO J Q,JIANG X H,SUN T F.Video human action recognition algorithm based on trained image CNN features[J].Computer Engineering,2017,43(11):234-238.(in Chinese) [19] 王晓晖,盛斌,申瑞民.基于深度学习的深度图超分辨率采样[J].计算机工程,2017,43(11):252-260. WANG X H,SHENG B,SHEN R M.Deep depth graph super resolution sampling based on depth learning[J].Computer Engineering,2017,43(11):252-260.(in Chinese) [20] DONG C,LOY C C,HE K,et al.Learning a deep convolutional network for image super-resolution[C]//Proceedings of 2014 European Conference on Computer Vision.Berlin,Germany:Springer,2014:184-199. [21] PRAKASH V,PRASAD K S,PRASAD T J C.Deep learning approach for image denoising and image demosaicing[J].International Journal of Computer Applications,2017,168:18-26. [22] TAN R,ZHANG K,ZUO W,et al.Color image demosaicking via deep residual learning[C]//Proceedings of 2017 IEEE International Conference on Multimedia and Expo.Washington D.C.,USA:IEEE Press,2017:793-798. [23] SHOPOVSKA I,JOVANOV L,PHILIPS W.RGB-NIR Demosaicing Using Deep Residual U-Net[C]//Proceedings of the 26th Telecommunications Forum.Washington D.C.,USA:IEEE Press,2018:1-4. [24] GOODFELLOW I,POUGET A J,MIRZA M,et al.Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.New York,USA:ACM Press,2014:2672-2680. [25] ZHANG K,ZUO W M,CHEN Y J,et al.Beyond a Gaussian denoiser:residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing,2017,26(7):3142-3155. [26] ALSAIARI A,RUSTAGI R,THOMAS M M,et al.Image denoising using a generative adversarial network[C]//Proceedings of the 2nd International Conference on Information and Computer Technologies.Washington D.C.,USA:IEEE Press,2019:126-132. [27] JOHNSON J,ALAHI A,LI F.Perceptual losses for real-time style transfer and super-resolution[C]//Proceedings of 2016 European Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2016:694-711. [28] MA K,DUANMU Z,WU Q,et al.Waterloo exploration database:new challenges for image quality assessment models[J].IEEE Transactions on Image Processing,2016,26(2):1004-1016. [29] WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612. |