A one-dimensional Sobolev representative is absolutely continuous. For , the fundamental theorem of calculus and Holder inequality give
Thus has a representative in . The qualification about representatives matters because a Sobolev space element is an almost-everywhere equivalence class.
In two dimensions, the Sobolev fundamental theorem of calculus on lines and Fubini's theorem imply that almost every horizontal and vertical slice belongs to and has this one-dimensional Hölder continuity. The slice seminorm depends on the slice; this does not give one uniform pointwise estimate on the square. For , Morrey's inequality additionally gives a globally Hölder continuous representative of exponent . For , global continuity need not hold. For example, with a smooth cutoff around an interior point, lies in when , but is unbounded. At , the cutoff version of is unbounded while its gradient has finite squared integral, since
These examples distinguish Sobolev slicing and planar continuity from a false two-dimensional application of the interval exponent.
Put . A BV space is a function whose distributional derivative is a finite vector-valued Radon measure. Equivalently its total variation seminorm is finite:
The BV space has norm . For , integration by parts against the compactly supported field gives . Conversely the measurable choice on nonzero gradients attains the pointwise bound. Approximating this bounded field by smooth fields, using interior cutoffs and the finite measure , justifies the supremum and gives
It is a norm of the derivative measure, rather than a pointwise derivative at jump discontinuities.
There is a genuine mismatch in the printed definition of the next functional. Its constraints on and are independent. Hence its stated supremum, denoted , separates as
The scalar supremum is , by cutoffs approaching one, and the vector supremum is the variation. For an affine image signal with , this gives , whereas the displayed square-root area would give . The intended relaxed graph-area functional instead uses the coupled pointwise constraint , giving
where is the singular part of . Both readings have a minimizer, but their equations are different.
Here is the direct method in the calculus of variations for either reading. Let be the literal or the corrected , and define the energy on , assigning infinity elsewhere. A minimizing sequence has bounded energy by comparison with . Both , so its variation is bounded, and the fidelity bounds , hence also and . By bounded-variation compactness, a subsequence converges strongly in to , and after another subsequence almost everywhere. Fatou's lemma proves
The regularizer is a supremum of affine functionals continuous in , since the test-field divergence is bounded. It is therefore lower semicontinuous. Combining the two lower bounds proves existence of a minimizer. In fact the convex regularizer and the strictly convex squared fidelity make the minimizer unique up to null sets. This does not assert that the minimizer must belong to .
For the intended graph area, conditionally assume that the minimizer is in . For , differentiate at . The derivative of the integrand is bounded by , so dominated convergence applies. The weak equation is
that is,
This is the graph-area Euler-Lagrange equation. Compactly supported variations impose no boundary condition in this statement.
For the literal printed supremum, the constant drops out and one obtains total variation denoising. Its total variation calibration form is
The distributional equation means . In particular wherever the gradient is nonzero; writing this quotient without handling zero gradients would be incomplete. Formally the one-sided derivative of is
Minimality in the directions and bounds the remaining linear functional by the second integral. The Hahn-Banach theorem extends it on that zero-gradient set to a bounded vector field of magnitude at most one, furnishing and the displayed weak equation. Thus the literal definition has a nonsmooth subgradient equation, not the square-root equation above.
On a bounded Lipschitz domain , the Rellich-Kondrachov compactness theorem states that is compact for and
with for . Thus when , every finite is allowed when , and the embedding into continuous functions with their uniform norm is compact when .
For the main proof, a Sobolev extension operator puts a bounded sequence into a common compactly supported region of . The Sobolev fundamental theorem of calculus on lines gives the uniform translation estimate . Convolution with a mollifier therefore approximates the sequence uniformly in Lp space. At any fixed smoothing scale, its derivatives and supremum are uniformly bounded, so the Arzelà-Ascoli theorem gives a convergent subsequence. A diagonal choice and the uniform approximation give strong Lp space convergence. The finite measure of handles , while Lp interpolation inequality with the bounded Sobolev embeddings upgrades this to the larger subcritical finite Lp spaces; for , Morrey's inequality gives uniform equicontinuity directly and the Arzelà-Ascoli theorem gives uniform convergence. Boundedness of the domain is essential.
Take the indicator function inside . For a compactly supported test vector field, integration in and then gives
The vertical derivative term integrates to zero. The absolute pairing is at most one, and test vector fields with along all but arbitrarily short endpoint pieces approach one. Thus . More explicitly, the distributional derivative is , where is Hausdorff measure along the segment. Also .
A Sobolev space function in has each weak derivative represented by an density with respect to planar Lebesgue measure. The nonzero segment measure here is singular with respect to that measure, so this requirement fails. Equivalently, the Sobolev fundamental theorem of calculus on lines would require an absolutely continuous representative on almost every horizontal slice, whereas each slice has a jump. Therefore
This is the bounded-variation step outside W11 example.
The Sobolev fundamental theorem of calculus on lines gives, for almost every ,
Apply the Minkowski integral inequality and translation invariance of Lebesgue measure:
The restriction on ensures that every translated copy used above lies in .