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The Iterated Tikhonov regularization update is the proximal operator stepThe objective has coercivity and is weakly sequentially lower semicontinuous and strongly convex, so the direct method in the calculus of variations and uniqueness of a minimizer of a strictly convex function give existence and uniqueness. Its gradient equation iswhich is exactly $(I+\tau K^{\ast }K)u=u_{\delta }^{(k)}+\tau K^{\ast }{f}^{\delta }$. The second term penalizes the change from the previous iterate, rather than the norm of $u$ relative to zero.

A function $f:C\to \mathbb{R}$ is strictly convex when, for distinct $x,y\in C$ and $0<t<1$,

Suppose distinct points $x$ and $y$ were both minimizers, with minimum value $m$. Since $C$ is a convex set, their midpoint belongs to $C$, while strict convexity givesa contradiction. Thus any minimizer is unique, which is the uniqueness of a minimizer of a strictly convex function.