Estimation of Depth Map Using Image Focus: A Scale-Space Approach for Shape Recovery

摘要: Laplacian-based derivatives used as a local focus measure to recover range information from an image stack have the undesirable effect of noise amplification, requiring good signal-to-noise ratios (SNRs) work well. Such requirement is challenged in practice by relatively low SNRs achieved under classical phase contrast microscopy and typically complex morphological structures (unstained) live cells. This paper presents results our recent on new, multiscale approach accurately estimate focal depth monolayer cell culture populated with moderately large number cells, whose boundaries were highly variable both terms size shape. The algorithm was constructed scale-space formalism which characterised adaptive smoothing capability that offers optimal filtration/sensitivity localisation accuracy. Moreover, it provides computationally scalable not only obviates need for additional heuristic procedures global thresholding (subsequent) interpolation focus-measure values, but also generates integral part algorithm, final image/map demonstrably more realistic and, perceptually, accurate.