Fourier Uncertainty Principles, Scale Space Theory and the Smoothest Average

Let $f \in L^{2}(\mathbb{R}^n)$ and suppose we are interested in computing its average at a fixed scale. This is easy: we pick the density $u_{}$ of a probability distribution with mean 0 and some moment at the desired scale and compute the convolution $u_{} * f$. Is there a particularly natural choice for $u$? This question is studied in scale space theory and the Gaussian is a popular answer. We were interested whether a canonical choice for $u$ can arise from a new axiom: having fixed a scale, the average should oscillate as little as possible, i.e. $u_{} = \arg\min_{u_{}} \sup_{f \in L^2(\mathbb{R}^n)} \frac{\| \nabla (u_{} *f) \|_{L^2(\mathbb{R}^n)}}{\|f\|_{L^2(\mathbb{R}^n)}}.$ This optimal function turns out to be a minimizer of an uncertainty principle: for $α> 0$ and $β> n/2$, there exists $c_{α, β,n} > 0$ such that for all $u \in L^1(\mathbb{R}^n)$ $\| |ξ|^β \cdot \widehat{u}\|^α_{L^{\infty}(\mathbb{R}^n)} \cdot \| |x|^α \cdot u \|^β_{L^1(\mathbb{R}^n)} \geq c_{α, β,n} \|u\|_{L^1(\mathbb{R}^n)}^{α+ β}.$ For $β= 1$, any nonnegative extremizer of the inequality serves as the best averaging function in the sense above, $β\neq 1$ corresponds to other derivatives. For $(n, β)=(1,1)$ we use the Shannon-Whittaker formula to prove that the characteristic function $u(x) = χ_{[-1/2,1/2]}$ is a local minimizer among functions defined on $[-1/2,1/2]$ for $α\in \left\{2,3,4,5,6\right\}$. We provide a sufficient condition for general $α$ in terms of a sign pattern for the hypergeometric function $_1F_2$.

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