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电子学报  2014 

基于全变分的全向图像稀疏重构算法

, PP. 243-249

Keywords: 折反射全向成像,压缩感知,图像重构,TV范数

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Abstract:

折反射全向成像由于曲面镜的反射作用,导致全向图像存在严重变形,传统的梯度计算方法在全向图像中不能很好地符合折反射成像的特点.为了从压缩采样数据快速有效地重构全向图像,提出了一种结合全向图像特征的全变分模型——全向全变分,并在基于TV范数进行全向图像重构时,采用全向全变分作为目标函数,进行模型的求解.实验结果验证了本文算法的有效性和可行性,其重构结果的主客观效果明显优于传统TV模型.

References

[1]  Boult T E, Gao X, Micheals R, et al.Omni-directional visual surveillance[J].Image and Vision Computing, 2004, 22(7):515-534.
[2]  杨鹏, 高晶, 刘作军, 等.基于全景与前向视觉的足球机器人定位方法研究[J].控制与决策, 2008, 23(1):75-78. Yang Peng, Gao Jin, Liu Zuo-jun, et al.Localization for robot soccer based on omni-vision and front-vision[J].Control and Decision, 2008, 23(1):75-78.(in Chinese)
[3]  Ikeuchi K, Sakauchi M, Kawasaki H, et al.Constructing virtual cities by using panoramic images[J].International Journal of Computer Vision, 2004, 58(3):237-247.
[4]  Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[5]  Candès E J, Romberg J, Tao T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory, 2006, 52(2):489-509.
[6]  Baraniuk R, Steeghs P.Compressive radar imaging[A].Proceedings of IEEE 2007 Radar Conference[C].Waltham, Massachusetts, 2007.128-133.
[7]  Lustig M, Donoho D L, Santos J M, et a1.Compressed sensing MRI[J].IEEE Signal Processing Magazine, 2008, 25(2):72-82.
[8]  Ma J.Single-pixel remote sensing[J].IEEE Geoscience and Remote Sensing Letters, 2009, 6(2):199-203.
[9]  石光明, 刘丹华, 高大化, 等.压缩感知理论及其研究进展[J].电子学报, 2009, 37(5):1070-1081. Shi Guang-ming, Liu Dan-hua, Gao Da-hua, et al.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica, 2009, 37(5):1070-1081.(in Chinese)
[10]  Romberg J.Imaging via compressive sampling[J].IEEE Signal Processing Magazine, 2008, 25(2):14-20.
[11]  Tropp J A.Greed is good:Algorithmic results for sparse approximation[J].IEEE Transactions on Information Theory, 2004, 50(10):2231-2242.
[12]  Rudin L, Osher S, Fatemi E.Nonlinear total variation based noise removal algorithms[J].Physica D:Nonlinear Phenomena, 1992, 60(1):259-268.
[13]  Candès E J, Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory, 2005, 51(12):4203-4215.
[14]  Candès E J, Tao T.Near optimal signal recovery from random projections:Universal encoding strategies[J].IEEE Transactions on Information Theory, 2006, 52(12):5406-5425.
[15]  Candès E J, Romberg J, Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theory, 2006, 52(2):489-509.
[16]  Bioucas-Dias J, Figueiredo M.A new TwIST:Two-step iterative thresholding algorithm for image restoration[J].IEEE Transactions on Image Processing, 2007, 16(12):2992-3004.
[17]  Bioucas-Dias J, Figueiredo M.Two-step algorithms for linear inverse problems with non-quadratic regularization[A].IEEE International Conference on Image Processing[C].San Antonio, TX, USA, 2007.
[18]  Becker S, Bobin J, Candès E J.NESTA:A fast and accurate first-order method for sparse recovery[J].Siam Journal on Imaging Sciences, 2011, 4(1):1-39.
[19]  Ma S, Yin W, Zhang Y, et al.An efficient algorithm for compressed MR imaging using total variation and wavelets[A].IEEE International Conference on Computer Vision and Pattern Recognition[C].Anchorage, Alaska, USA, 2008.1-8.
[20]  Yang J, Zhang Y, Yin W.A fast alternating direction method for TVL1-L2 signal reconstruction from partial fourier data[J].IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2):288-297.
[21]  Li C B.An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing[D].Master thesis, Rice University, USA, 2009.
[22]  谭树人, 林高鹏, 张茂军.基于位置分辨率的折反射全向图像邻域定义[J].电子学报, 2011, 39(1):201-206. Tan Shu-ren, Lin Gao-peng, Zhang Mao-jun.Neighborhood definition for catadioptric omnidirectional image based on resolution of position[J].Acta Electronica Sinica, 2011, 39(1):201-206.(in Chinese)
[23]  焦李成, 杨淑媛, 刘芳, 等.压缩感知回顾与展望[J].电子学报, 2011, 39(7):1651-1662. Jiao Li-cheng, Yang Shu-yuan, Liu Fang, et al.Development and prospect of compressive sensing[J].Acta Electronica Sinica, 2011, 39(7):1651-1662.(in Chinese)
[24]  Baraniuk R.A lecture on compressive sensing[J].IEEE Signal Processing Magazine, 2007, 24(4):118-121.
[25]  Donoho D L.For most large underdetermined systems of linear equations, the minimal L1 norm solution is also the sparsest solution[J].Communications on Pure and Applied Mathematics, 2006, 59(6):797-829.

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