Multivariate super-resolution without separation

Abstract

Abstract In this paper, we study the high-dimensional super-resolution imaging problem. Here, we are given an image of a number of point sources of light whose locations and intensities are unknown. The image is pixelized and is blurred by a known point-spread function arising from the imaging device. We encode the unknown point sources and their intensities via a non-negative measure and we propose a convex optimization program to find it. Assuming the device’s point-spread function is componentwise decomposable, we show that the optimal solution is the true measure in the noiseless case, and it approximates the true measure well in the noisy case with respect to the generalized Wasserstein distance. Our main assumption is that the components of the point-spread function form a Tchebychev system ($T$-system) in the noiseless case and a $T^{*}$-system in the noisy case, mild conditions that are satisfied by Gaussian point-spread functions. Our work is a generalization to all dimensions of the work [14] where the same analysis is carried out in two dimensions. We also extend results in [27] to the high-dimensional case when the point-spread function decomposes.

More by Bakytzhan Kurmanbek

• Deep Learning Approaches for Autoregressive Fracture Network Path Prediction: A Comprehensive Study on the Teapot Dome DatasetIEEE Access · 2026
• Explicit inverse of symmetric, tridiagonal near Toeplitz matrices with strictly diagonally dominant Toeplitz partSpecial Matrices · 2025
• Prediction of natural fracture network patterns using feature engineering and machine learning approachesComputational energy science. · 2024 · Citations: 2
• Explicit inverse of symmetric, tridiagonal near Toeplitz matrices Part II: with weakly diagonally dominant ToeplitzarXiv · 2024