The capability of coherence scanning interferometry has been extended recently to include the determination of the interfacial surface roughness between a thin film and a substrate when the surface perturbations are less than ∼10  nm in magnitude. The technique relies on introducing a first-order approximation to the helical complex field (HCF) function. This approximation of the HCF function enables a least-squares optimization to be carried out in every pixel of the scanned area to determine the heights of the substrate and/or the film layers in a multilayer stack. The method is fast but its implementation assumes that the noise variance in the frequency domain is statistically the same over the scanned area of the sample. This results in reconstructed surfaces that contain statistical fluctuations. In this paper we present an alternative least-squares optimization method, which takes into account the distribution of the noise variance-covariance in the frequency domain. The method is tested using results from a simulator and these show a significant improvement in the quality of the reconstructed surfaces.