Sparse partial derivatives and reconstruction from partial Fourier data

Signal reconstruction from the smallest possible Fourier measurements has been a key motivation in the compressed sensing research. We present an approach that exploits the interdependency and structural sparsity of partial derivatives for lowering the sampling rates necessary for accurate reconstruction. Our experiments show that for signals that are sparse in the gradient domain our proposed method significantly outperforms the existing approaches including the total variation (TV) based CS reconstruction.