1. 毕业设计（论文）主要目标：
1.了解图像超分辨率重建的基本原理；
2.了解传统的超分辨率重建算法；
3.掌握基于深度卷积网络的图像超分辨率重建算法；
2. 毕业设计（论文）主要内容：
本毕业论文主要研究一种基于深度卷积网络的图像超分辨率重建算法，具体内容包括:
(1)阐述超分辨率重建的基本原理；
(2)综述传统超分辨率重建算法及其应用与社会意义以及基于深度卷积网络的图像超分辨率重建算法；
3. 主要参考文献
[1].S.Borman and R.L.Stevenson. Super-Resolution from Image Sequences-A Review. Midwest Symposium on Circuits and Systems,pages 374-378,1998.[2].H.Chang,D.-Y. Yeung,and Y.Xiong. Super-resolution through neighbor embedding. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition,volume 1,page 1-1.IEEE,2004.2,7[3].Z.Cui,H.Chang,S.Shan,B.Zhong,and X.Chen. Deep network cascade for image super-resolution.In European Conference on Computer Vision,page 49-64.Springer,2014[4].D.Dai,R.Timofte,and L.Van Gool. Jointly optimized regressors for image super-resolution.In Eurographics,volume7,page 8,2015.2,4 [5].C.Dong,C.C.Loy,K.He,and X.Tang.Learning a deep convolutionl network for image super-resolution.In European Conference on Computer Vision,pages 184-199.Springer,2014.3,6,7[6]. C.Dong,C.C.Loy,K.He,and X.Tang. Image super-resolution using deep convolutional networks.IEEE Transactions on Pattern Analysis and Machine Intelligence,2015.2,3,4,5,6,7,8[7].N.Efrat,D.Glasner,A.Apartsin,B.Nadler,and A.Levin. Accurate blur models vs. image priors in single image super-resolution.In IEEE International Conference on Computer Vision pages 2832-2839.IEEE,2013.2[8].S.Farsiu,M.D.Robinson,M.Elad,and P.Milanfar. Fast and robust multiframe super resolution.IEEE Trnsactions on Image Processing,13(10):1327-1344,2004.[9].C.Fernandez-Granda and E.Candes. Super-resolution via transform-invariant group-sparse regularization.In IEEE International Conference on Computer Vision pages 3336-3343.IEEE,2013.2[10].X.Gao,K.Zhang,D.Tao,and X.Li. Image super-resolution with sparse neighbor embedding.IEEE Transactions on Image Processing,21(7):3194-3205,2012.2
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