Suppression of noise in SEM images using weighted local hysteresis smoothing filter

Abstract

It has been proven that Hysteresis Smoothing (HS) has several advantages for Scanning Electron Microscopy (SEM) image noise reduction. HS uses hysteresis thresholding to remove noise besides preserving important details of images. Determination of optimal threshold values (cursor width) plays an effective role in improving the performance of HS based filters. Recently, a novel local technique, named Local Adaptive Hysteresis Smoothing (LAHS), has been proposed to compute an optimal cursor width. In this paper, a new method is proposed to improve the performance of LAHS in noise reduction and detail preservation. In the proposed approach which is based on weighted averaging, local statistical characteristics of the image are used in order to modify the final values of estimated pixels by LAHS method. Proposed method is applied to SEM images corrupted by different levels of noise. Noise reduction and detail preservation performance of the proposed method is compared in both objective and subjective manners with other HS based filters. Experimental results demonstrate that the proposed method is successful in improving the performance of LAHS and also it achieves better performance in noise reduction besides detail preservation of SEM images in comparison with other HS based filters. SCANNING 38:634-643, 2016. © 2016 Wiley Periodicals, Inc.

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

“…There are several approaches currently employed in literature for processing noisy SEM images. They include spatial filtering approaches, including Gaussian, median, curvature, anisotropic diffusion, wavelet, adaptive wiener filter, and hysteresis smoothing [66]- [69]. Simple high-frequency filtering and DL-based denoising approaches have also been used on SEM images [38].…”

Preservation of image edge feature based on snowfall model smoothing filter

“…Usually, image noise is mainly composed of high-frequency component; smoothing filter can enhance image low-frequency component to remove the high-frequency noise [3][4][5]. The typical smoothing process includes mean filter, median filter, and Gaussian low-pass filter.…”