Let , and , where is the local flow of with nonnegative . The formula uses common oriented BV traces on a hypersurface; the trace difference of is zero almost everywhere on . It also holds for both negative-time increments with denominator positive .
The BV slicing theorem reduces the calculation to one dimension. With a right-continuous representative of and , Fubini's theorem yieldsThe inner factor tends to and is bounded in absolute value by . Dominated convergence against leaves only its measure atoms at the countable slice jumps of . That same bound times permits integration over the transverse coordinates. No essential bound on is used, even if its slice values grow without bound. The formula is useful for opposite-flow fidelity identity for quadratic data with unbounded data and a bounded comparison function.
Assume minimizes scalar total variation denoising for . The data may be unbounded. For the local flow of , use as two competitors. The total variation under opposite smooth flows and convexity of the total variation seminorm bound the sum of regularizer changes by . Minimality and the opposite-flow fidelity identity for quadratic data, evaluated using the BV jump-product limit with one bounded factor, implyLet . The integrands define finite signed Radon measures, since is bounded and the jump variation of is finite. Arbitrary nonnegative smooth make their densities nonnegative. Testing every coordinate direction removes the factor and proves the stated inequality.
Past exam of the mathematics course of the University of Cambridge 2014 iii Paper 64 2 iii Solution Created 2026-10-03 Updated 2026-10-07
The literal assumptions suffice for the scalar quadratic fidelity problem. One can avoid regularity of level-set boundaries by moving only a clipped part of the minimizer. We prove the stronger jump-amplitude inequality for total variation denoising:Comparison with the zero function gives finite energy and hence . Here , and both differences use the same oriented BV traces on a hypersurface. Reversing the normal reverses both differences and leaves the inequality unchanged. Outside , the two traces of agree, so the inequality would read at a jump of . It therefore gives the requested no-new-jumps property of total variation denoising in every dimension.
First use residual-preserving clipping of an ROF minimizer. For an integer , putThe coarea formula for BV functions gives scalar total variation splitting under clipping:Indeed the levels in contribute to , while the levels outside that interval contribute to . This uses the full signed coarea formula. For any , minimality of and the triangle inequality for the total variation seminorm giveCancel the tail variation. Thus is a bounded ROF denoising minimizer for . The data may still be unbounded. This is an exact reduction preserving ; it does not truncate the data and then pass to a limit of different reconstructions.
We next prove the jump-amplitude inequality for a bounded ROF minimizer, allowing its data to be unbounded. Fix a coordinate , a nonnegative , and let be the local flow of the smooth vector field . The flow is the identity near the domain boundary, preserves each line parallel to , and satisfies . WriteThese maps are diffeomorphisms, with and uniformly.
The useful trace calculation is the BV jump-product limit with one bounded factor. For and ,The right side is integrable: and only the common jump part of contributes. The same limit holds with both increments replaced by their negative-time increments, still dividing by positive .
Here is why this calculation needs only one bounded factor. By the BV slicing theorem, almost every coordinate slice of and has one-sided representatives. On one such interval let and use its right-continuous representative. Since ,Fubini's theorem rewrites the slice integral asAt each interior , the inner expression tends to ; if , it is zero. Its absolute value is at most . Dominated convergence against therefore leaves just the measure atoms common to the two slices. The bound is also integrable over the transverse coordinates, by the BV slicing theorem. Integrating the slice jump sums gives the surface integral and its factor . Negative-time increments give the same product, because both slice differences reverse sign. At no point is a uniform bound on across the slices required.
For , use the mixed competitorsThe total variation under opposite smooth flows satisfiesTo see this for the entire vector Radon measure , the change of variables formula givesFor , the two cofactor matrices expand as , with the same and opposite signs. Their norm expansions on have cancelling linear terms. Integrating proves the estimate, including the absolutely continuous, jump and Cantor parts. The BV transformation formula is also given in Lemma 4.2 on differentiable regularizers; that lemma does not assume bounded data. The total variation seminorm is a convex function, soThus minimality forces the sum of the two fidelity changes to have nonnegative limit after division by .
It remains to evaluate that sum without bounding . Put . Exact expansion of the quadratic fidelity, together with change of variables, gives the opposite-flow fidelity identity for quadratic data:For completeness, the cross-term identity fixing its sign isThe last integral is : , whileTake first, then . Here and the local flow is strongly continuous in . The remaining Jacobian mass term is . This argument avoids multiplying an unbounded fidelity derivative by an uncontrolled derivative measure.
Apply the BV jump-product limit with one bounded factor with and , and use minimality. For every nonnegative and every coordinate ,Let . These are inequalities for finite signed Radon measures, so arbitrary nonnegative smooth tests imply nonnegativity of their densities. Since at least one coordinate of a unit normal is nonzero,This proves the bounded-minimizer lemma with arbitrary data.
Finally return to and . At almost every finite approximate jump point of , choose an integer . The BV traces on a hypersurface commute with clipping, so , and there. It is consequently a jump point of , and its inequality is exactly . A countable union over removes all exceptional surface-null sets. Outside , the BV traces on a hypersurface of agree almost everywhere. We concludeThe mechanism is exact scalar coarea splitting, paired smooth-flow variations, a one-bounded-factor BV trace limit, and localization through integer clipping levels. It works in every dimension under the printed hypotheses, without essential boundedness of or and without regularity of their level-set boundaries.
In total variation calibration notation, . Since this divergence is itself in the BV space, the proved inequality equivalently readsThus an upward output jump forces the appropriate nonpositive jump of the calibrated divergence. This is a consequence of the variational argument above, with common oriented BV traces on a hypersurface; no curvature of the rectifiable interface or differentiability of its normal is assumed.