An asymptotic analysis of separating pointlike and $C^{\beta}$-curvelike singularities

In this paper, we present a theoretical analysis of separating images consisting of pointlike and $C^{ \beta}$-curvelike structures, where $\beta \in (1,2] $. Our approach is based on $l_1$-minimization, in which the sparsity of the desired solution is exploited by two sparse representation systems. It is well known that for such components wavelets provide an optimally sparse representation for point singularities, whereas $\alpha$-shearlet type with $\alpha$=$\frac{2}{\beta}$ might be best adapted to the $C^{\beta}$-curvilinear singularities. In our analysis, we first propose a reconstruction framework with a theoretical guarantee on convergence, which is extended to use general frames instead of Parseval frames. We then construct a dual pair of bandlimited $\alpha$-shearlets which possesses a good time and frequency localization. Finally, we apply the result to derive an asymptotic accuracy of the reconstructions. In addition, we show that it is possible to separate these two components as long as $\alpha <2$, i.e., bandlimited $\alpha$-shearlets which range from wavelet to shearlet type do not coincide with wavelets in the sense of isotropic fashion.