A mathematical theory of super-resolution and two-point resolution

This paper focuses on the fundamental aspects of super-resolution, particularly addressing the stability of super-resolution and the estimation of two-point resolution. Our first major contribution is the introduction of two location-amplitude identities that characterize the relationships between locations and amplitudes of true and recovered sources in the one-dimensional super-resolution problem. These identities facilitate direct derivations of the super-resolution capabilities for recovering the number, location, and amplitude of sources, significantly advancing existing estimations to levels of practical relevance. As a natural extension, we establish the stability of a specific $l_{0}$ minimization algorithm in the super-resolution problem.The second crucial contribution of this paper is the theoretical proof of a two-point resolution limit in multi-dimensional spaces. The resolution limit is expressed as $$\begin{align*}\mathscr R = \frac{4\arcsin \left(\left(\frac{\sigma}{m_{\min}}\right)<^>{\frac{1}{2}} \right)}{\Omega} \end{align*}$$ for ${\frac {\sigma }{m_{\min }}}{\leqslant }{\frac {1}{2}}$ , where ${\frac {\sigma }{m_{\min }}}$ represents the inverse of the signal-to-noise ratio ( ${\mathrm {SNR}}$ ) and $\Omega $ is the cutoff frequency. It also demonstrates that for resolving two point sources, the resolution can exceed the Rayleigh limit ${\frac {\pi }{\Omega }}$ when the signal-to-noise ratio (SNR) exceeds $2$ . Moreover, we find a tractable algorithm that achieves the resolution ${\mathscr {R}}$ when distinguishing two sources.