Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
Major
Mathematics
College
College of Arts and Sciences
Research Mentor
Hyeona Lim
Research Mentor's Department
Department of Mathematics and Statistics
Research Center
Center for Computational Sciences
Abstract
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
Current image denoising techniques can be broadly categorized into two types: variational methods which use minimizing functionals, and filtering methods via pixel averaging. Specifically, the non-local means (NLM) filtering based image denoising algorithm involves replacing each pixel value by the weighted averages of all pixels in the entire image. The basic non-local means algorithm is predicated on manually inputting a parameter to determine the weight of each pixel. We introduce and analyze a new method of implementing the weight factors, based on small variances in the pixel values to identify noise. The new method will identify newly classified noisy pixels, then while comparing blocks of pixels for similarity it is possible to disregard noisy pixels and gain a clearer comparison of structure for weighting. The new method is numerically tested and compared to conventional NLM based methods via the peak signal to noise ratio (PSNR), and visual comparison. The results show that the new method is preferable to the current NLM methods.
Presentation Date
Summer 8-2-2024
Keywords
image processing, image denoising, computational mathematics
Recommended Citation
Carroll, Ely and Lim, Hyeona, "New Weighting Parameters for Non-Local Means based Denoising Algorithm" (2024). Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science. 1.
https://scholarsjunction.msstate.edu/ccs-reu/1