Mumford–Shah functional ($\int (u-g{)}^2+\alpha \int |\nabla u{|}^2+\beta {\mathcal{H}}^1(J_u)$)

The energy ${\int }_{\Omega \setminus K}(u-g{)}^2+\alpha {\int }_{\Omega \setminus K}|\nabla u{|}^2+\beta {\mathcal{H}}^1(K)$ balances data fidelity, within-region smoothness and image edge length. In its relaxed SBV space formulation the image edge set is $J_u$. Clipping to the bounded data range, the SBV compactness theorem and lower semicontinuity prove existence; the full segmentation problem is not strictly convex.

The Mumford–Shah functional can be approximated by an auxiliary edge field $0\le v\le 1$ and energyThe field is near one inside regions and near zero across an image edge, weakening smoothing there. The profile $v(s)=1-e^{-|s|/(2\epsilon )}$ has unit edge energy per unit length in this normalization. The 1990 construction is a Gamma-convergence approximation; its global-minimizer limit does not guarantee global optimality of an alternating numerical solve. Each fixed-field subproblem is quadratic, but the joint energy is nonconvex.

Removing the jump-length cost from the Mumford–Shah functional lets finer piecewise-constant partitions reduce squared fidelity without charging for their proliferating image edges. For continuous data on a compact image domain, fine-cell averages converge in squared error while the ordinary within-cell gradient is zero. A finite interface penalty makes that geometry costly.

On the unit square, let $g=0$ and take $u_0={\chi }_{\{x_1>0.4\}}$, $u_1={\chi }_{\{x_1>0.6\}}$. Both have one unit-length jump and zero ordinary gradient. Their mean has two unit-length jumps. For squared-error coefficient one, $[E(u_0)+E(u_1)]/2=0.5+\beta$ while $E((u_0+u_1)/2)=0.45+2\beta$, violating convexity when $\beta >0.05$. The Mumford–Shah functional can therefore be nonconvex even though the fixed-edge reconstruction subproblem is strictly convex.

For a fixed sufficiently regular image edge set, the Mumford–Shah functional is a strictly convex quadratic energy on the Sobolev space of its complement. A first variation gives $u-\alpha \Delta u=g$ within each region and separate one-sided Neumann boundary conditions on free edges. A jump need not vanish across an edge. The weak solution is unique by the Lax-Milgram theorem; this does not prove uniqueness when the edge set is also optimized.

As the jump-length weight tends to infinity with the gradient weight fixed, the limiting problem minimizes $\Vert u-g{\Vert }_2^2+\alpha \Vert \nabla u{\Vert }_2^2$ on $H^1$. It has a unique minimizer by the Lax-Milgram theorem and strict convexity. Without prescribed boundary values, its weak equation is $u-\alpha \Delta u=g$ with natural Neumann boundary conditions.

The infinite gradient-weight limit restricts to SBV space image signals with $\nabla u=0$ almost everywhere. Their energy is fidelity plus jump length, represented by constants on a Caccioppoli partition. Internal perimeter is counted once by $\frac{1}{2}{\sum }_i\mathrm{Per}(E_i;\Omega )$; adjacent equal-valued regions can be merged.

Suppose two constant data intensities differ by $d$, have areas $a$ and $A-a$, and share an internal boundary of length $L$. Keeping the boundary costs $\beta L$ with zero fidelity; merging the two regions costs $a(A-a)d^2/A$ after fitting their common mean. The displayed inequality makes splitting cheaper among these two candidate geometries. This comparison does not prove either geometry is globally optimal among all partitions.

When grey values are fixed but region labels vary, a smooth interface satisfies $\beta \kappa =(c_j-g{)}^2-(c_i-g{)}^2$. The curvature uses the normal pointing out of region $i$, positive on an outward-oriented circle. It balances the change in data cost against the first variation of interface length. Fixing a full spatial image signal instead fixes its jumps and leaves no such boundary-relocation freedom.

At a freely moving regular junction of three smooth image edges with equal isotropic length weights, the endpoint first variation requires the sum of their outward unit tangent vectors to vanish. Three unit vectors with zero sum have pairwise angles of $120$ degrees. Different interface weights instead give a weighted force balance, and constrained or singular junctions require separate hypotheses.

For a fixed positive-area region $E_i$, its least-squares grey value is $c_i=|E_i{|}^{-1}{\int }_{E_i}g$. The minimum fidelity equals $\int g^2-{\sum }_i({\int }_{E_i}g{)}^2/|E_i|$. This eliminates the grey values before optimizing the partition geometry.

A minimizer of the Mumford–Shah functional with bounded image signal data has an essentially closed jump set: replacing $J_u$ by its relative closure adds no $(n-1)$-dimensional measure. Its complement supports a Sobolev representative. This regularity theorem connects a relaxed SBV space minimizer to the classical closed-edge formulation; it is not a property of every special bounded-variation function.