Self-adaptive sampling rate assignment and image reconstruction via combination of structured sparsity and non-local total variation priors
We propose a novel framework of adaptive sensing and reconstruction. The blocks' compressibility is estimated from pre-sensed measurements. The sampling rate is assigned based on the estimated blocks' compressibility. Structured sparsity prior and non-local total variation prior are employed in reconstruction.
structured sparsity non-local total variation compressibility compressive sampling assignment of sampling rate