Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 4, Pages 736-740Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2360457
Keywords
Compressed sensing; image processing
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Funding
- National Natural Science Foundation of China [61272314]
- Beijing Natural Science Foundation [4141003]
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Basic compressed-sensing algorithms for image reconstructions mainly deal with the computation of sparse regularization. Remote sensing applications often have multisource or multitemporal images whose different components are acquired separately. Therefore, this letter considers the reconstruction of a remote sensing image using an auxiliary image from another sensor or another time as the reference. For this application, a new compressed-sensing object function is developed that uses a reference image as a prior. In the new model, the sparsity constraints in the transform domain come from the target image, and the gradient priors in the spatial domain come from the auxiliary reference image. The hybrid regularization is optimized by basing the algorithm on the Bregman split method. The proposed method shows better performances when compared with other three popular compressed-sensing algorithms.
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