Image Fusion and Reconstruction of Compressed Data: A Joint Approach

In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach.

[1]  Hairong Qi,et al.  The design and evaluation of a generic method for generating mosaicked multispectral filter arrays , 2006, IEEE Transactions on Image Processing.

[2]  Laurent Condat,et al.  Discrete Total Variation: New Definition and Minimization , 2017, SIAM J. Imaging Sci..

[3]  Laurent Condat,et al.  Color filter array design using random patterns with blue noise chromatic spectra , 2010, Image Vis. Comput..

[4]  I. M. Otivation.  Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems , 2018 .

[5]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[6]  Ashwin A. Wagadarikar,et al.  Single disperser design for coded aperture snapshot spectral imaging. , 2008, Applied optics.

[7]  Jocelyn Chanussot,et al.  Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing , 2014, Proceedings of the IEEE.

[8]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Junichi Nakamura,et al.  Image Sensors and Signal Processing for Digital Still Cameras , 2005 .

[10]  Henry Arguello,et al.  Code aperture optimization for spectrally agile compressive imaging. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Henry Arguello,et al.  Compressive hyperspectral and multispectral imaging fusion , 2016, SPIE Defense + Security.

[12]  Masatoshi Okutomi,et al.  A Practical One-Shot Multispectral Imaging System Using a Single Image Sensor , 2015, IEEE Transactions on Image Processing.

[13]  Laurent Condat.  A new random color filter array with good spectral properties , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[14]  Laurent Condat,et al.  A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization , 2014, IEEE Signal Processing Letters.

[15]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[16]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[17]  Shen-En Qian.  Satellite Data Compression , 2013 .

[18]  Martin Sweeting,et al.  Image compression systems on board satellites , 2009 .

[19]  Laurent Condat,et al.  A New Color Filter Array With Optimal Properties for Noiseless and Noisy Color Image Acquisition , 2011, IEEE Transactions on Image Processing.

[20]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[21]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[22]  Patrick J. Wolfe,et al.  Spatio-Spectral Color Filter Array Design for Optimal Image Recovery , 2008, IEEE Transactions on Image Processing.

[23]  Eric Dubois,et al.  A Geometric Method for Optimal Design of Color Filter Arrays , 2011, IEEE Transactions on Image Processing.

[24]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[25]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[26]  Laurent Condat,et al.  A generic variational approach for demosaicking from an arbitrary color filter array , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[27]  Holger Rauhut,et al.  A Mathematical Introduction to Compressive Sensing , 2013, Applied and Numerical Harmonic Analysis.

[28]  Henry Arguello,et al.  Compressive Coded Aperture Spectral Imaging: An Introduction , 2014, IEEE Signal Processing Magazine.