Signal-to-noise ratio estimation technique for SEM image using linear regression

This paper proposes a new signal-to-noise ratio (SNR) estimation technique on scanning electron microscope (SEM) image, using linear regression. The method is based on the single image approach. Four good quality images are used to compare the proposed method and the existing methods: nearest neighborhood, first order interpolation and piecewise cubic Hermite interpolation. The results are compared in terms of estimation peaks, SNR and SNR in dB. In this paper four random selected images are used to present the performance of the proposed method. The method gives better estimation compared to existing methods. Statistical test shows that the estimation results are similar to the original.