Hyperspectral image inpainting based on collaborative total variation

Inpainting in hyperspectral imagery is a challenging research area and several methods have been recently developed to deal with this kind of data. In this paper we address missing data restoration via a convex optimization technique with regularization term based on Collaborative Total Variation (CTV). In particular we evaluate the effectiveness of several instances of CTV in conjunction with different dimensionality reduction algorithms.

[1]  Nelly Pustelnik,et al.  A Nonlocal Structure Tensor-Based Approach for Multicomponent Image Recovery Problems , 2014, IEEE Transactions on Image Processing.

[2]  Michael Möller,et al.  Collaborative Total Variation: A General Framework for Vectorial TV Models , 2015, SIAM J. Imaging Sci..

[3]  Mark Andrews,et al.  Total Variation Regularization via Continuation to Recover Compressed Hyperspectral Images , 2015, IEEE Transactions on Image Processing.

[4]  Liangpei Zhang,et al.  Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[6]  Tony F. Chan,et al.  Color TV: total variation methods for restoration of vector-valued images , 1998, IEEE Trans. Image Process..

[7]  Robert S. DiPietro,et al.  Long-Wave Infrared Hyperspectral Remote Sensing of Chemical Clouds: A focus on signal processing approaches , 2014, IEEE Signal Processing Magazine.

[8]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[9]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[10]  Jian Zhang,et al.  Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  Amandine Robin,et al.  Determining the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory , 2013, IEEE Transactions on Image Processing.

[12]  Haida Liang,et al.  Advances in multispectral and hyperspectral imaging for archaeology and art conservation , 2012 .

[13]  Elena Deza,et al.  Dictionary of distances , 2006 .

[14]  Gang Yang,et al.  Missing Information Reconstruction of Remote Sensing Data: A Technical Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[15]  Yi Chang,et al.  Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping , 2015, IEEE Transactions on Image Processing.

[16]  Emma Villeneuve,et al.  Nonlinear Deconvolution of Hyperspectral Data With MCMC for Studying the Kinematics of Galaxies , 2014, IEEE Transactions on Image Processing.

[17]  Christine Guillemot,et al.  Image Inpainting : Overview and Recent Advances , 2014, IEEE Signal Processing Magazine.

[18]  J. Boardman,et al.  Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm , 1992 .

[19]  Arif Mahmood,et al.  Hyperspectral Face Recognition With Spatiospectral Information Fusion and PLS Regression , 2015, IEEE Transactions on Image Processing.

[20]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[21]  Yin Zhang,et al.  A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing , 2012, IEEE Transactions on Image Processing.

[22]  Tuan Vo-Dinh.  A hyperspectral imaging system for in vivo optical diagnostics , 2004, IEEE Engineering in Medicine and Biology Magazine.