 We compare quantitatively our results with several convex, nonconvex and deep-learning-based approaches for several synthetic and real-world data. Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
<clarity>  We compare quantitatively our results with several convex, nonconvex and deep-learning-based approaches for several synthetic and real-world data. Our numerical results show that combining super-resolution with gradient sparsity is particularly helpful for object detection and labelling tasks (such as QR scanning and land-cover classification), for which our results are shown to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .
 We compare quantitatively our results with several variational approaches as well as with state-of-the-art deep-learning techniques. Our experiments show that \ell^0 gradient-regularised super-resolved images can be effectively used to improve the classification precision of standard clustering algorithms and state-of-the art deep architectures 3 .