Signal‐to‐noise ratio estimation on SEM images using cubic spline interpolation with Savitzky–Golay smoothing

Abstract

SummaryA new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

“…In the previous work the SNR of SEM images was improved by using smoothing function, which is based on least square by fitting a small set of successive data point into a polynomial (Zulfiqar et al ., 2005). The estimated central of the fitted point polynomial curve is considered as a new smoothed data set (Sim et al ., ).…”

“…Savitzky–Golay smoothing shows that the set of digits can be defined as weighting coefficients but based on desired Span level and polynomial degree to perform the smoothing process (Sim et.al ., ). These digits can be created as precisely equivalent for fitting the data to a polynomial in order to minimize the error in SNR estimation (Gorry, ).…”

“…, denotes a set of smoothed data points with third order of polynomial that is produced by cubic spline interpolation, and is the weighting factor in smoothing the window (Sim et.al ., ). To estimate the span level of Savitzky–Golay smoothing, we used a third degree polynomial curve fitting to obtain an expression for Span‐level estimation (Sim et al ., ). The expression is shown in Eq.…”

“…Savitzky–Golay smoothing filter constructs the signal curvature with weighting factor specified by unweighted linear least squares regression and third order of polynomial (Sim et.al ., ). In this paper, we develop proposed method by merging CSISG method with WLSE filter at following section.…”

“…In this paper, based on our previous work cubic spline interpolation with Savitzky–Golay (CSISG; Sim et al ., ), a noise reduction filtering has developed using weighted least squares error (WLSE) technique. The enhanced filter is analysed with extant filters in terms of signal‐to‐noise ratio, the subjective look of the resulting image and loss of resolution.…”