An adaptive parallel method for denoising microorganism image from scanning electron microscope

Abstract

Scanning Electron Microscope (SEM) has been used in many scientific areas especially in biomedical and microbiology for observing and studying microorganisms, cells structure, and plant fibers. The images retrieved from SEM are usually contaminated with noises. In this paper, we propose the new adaptive framework for denoising the image retrieved from a SEM. The well-denoised SEM images will assist the biologist and microbiologist in studying microorganism parts especially cell wall and particles on them. Normally, the image denoising process is time-consuming. The fast denoising process will further speed up the image analyzing process in microorganism studies. In this work, we use the new variation denoising model which can effectively remove both additive and multiplicative noises at the same time. We speed up the denoising process by quickly partitioning the unused background from the images of microorganism bodies. We apply a gold coating on the microorganism before examining it through a SEM to create a high intensity output image of the microorganism to make partitioning the image of the microorganism from the background simple and accurate. For denser images with less background, applying the partitioning technique is challenging and may not be efficient. In such cases, we applied the Sliding Window Gauss-Seidel parallel algorithm to efficiently denoise the partitioned images. From the experiments, our denoising process based on multi-core processing is efficient and can process 1332×1000 images at up to 2.98 images per second. Our model can efficiently decrease time spend on denoising our sample images from SEM by up to 94.48% on quad-core processor and the denoised image quality is satisfactory and can reveal some important details for biologists and microbiologists. The denoised images in this experiment will be used for further studies of cell wall and leakage of cytoplasm affected by electricity. Copyright © 2013 American Scientific Publishers.