Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach
Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints
SIAM Journal on Numerical Analysis
Model-based compressive sensing
IEEE Transactions on Information Theory
Data compression and harmonic analysis
IEEE Transactions on Information Theory
Simultaneous multichannel image restoration and estimation of the regularization parameters
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization
IEEE Transactions on Image Processing
A Unified Approach to Superresolution and Multichannel Blind Deconvolution
IEEE Transactions on Image Processing
A new look at spotlight mode synthetic aperture radar as tomography: imaging 3-D targets
IEEE Transactions on Image Processing
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We consider the problem of joint enhancement of multichannel images with pixel based constraints on the multichannel data. Previous work by Çetin and Karl introduced nonquadratic regularization methods for SAR image enhancement using sparsity enforcing penalty terms. We formulate an optimization problem that jointly enhances complex-valued multichannel images while preserving the cross-channel information, which we include as constraints tying the multichannel images together. We pose this problem as a joint optimization problem with constraints. We first reformulate it as an equivalent (unconstrained) dual problem and develop a numerically-efficient method for solving it. We develop the Dual Descent method, which has low complexity, for solving the joint optimization problem. The algorithm is applied to both an interferometric synthetic aperture radar (IFSAR) problem, in which the relative phase between two complex-valued images indicate height, and to a synthetic multimodal medical image example.