Abstract—Surface metrology is the science of measuring smallscale
features on surfaces. In this paper, a novel compressed
sensing (CS) theory is introduced for the surface metrology to
reduce data acquisition. We first describe that the CS is naturally
fit to surface measurement and analysis. Then, a geometricwavelet-
based recovery algorithm is proposed for scratched and
textural surfaces by solving a convex optimal problem with sparse
constrained by curvelet transform and wave atom transform.
In the framework of compressed measurement, one can stably
recover compressible surfaces from incomplete and inaccurate
random measurements by using the recovery algorithm. The necessary
number of measurements is far fewer than those required
by traditional methods that have to obey the Shannon sampling
theorem. The compressed metrology essentially shifts online measurement
cost to computational cost of offline nonlinear recovery.
By combining the idea of sampling, sparsity, and compression, the
proposed method indicates a new acquisition protocol and leads to
building new measurement instruments. It is very significant for
measurements limited by physical constraints, or is extremely expensive.
Experiments on engineering and bioengineering surfaces
demonstrate good performances of the proposed method.
Index Terms—Compressed sensing (CS)/compressive sampling,
curvelets, incomplete measurement, sparse recovery, surface characterization,
surface metrology, wave atoms.