期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130472
语种：English
出版社：Science and Information Society (SAI)
摘要：The accurate approximation of pixel value for preserving image details at a high concentration of noise has led the researchers to improve filters performance. A few image restoration filters are effective at lower density noise. Filters are commonly deployed for cameras, image processing tasks, medical image analysis, guided media data transmission, and real-time machine learning. This article proposes a mathematical model for the exact pixel value estimation at a high noise density for RGB and Gray images. The mathematical model is implemented to fuse statistical reasoning on the optimized mask sizes while preserving image details. Different parameter returns from the median filter, the trimmed median filter, the trimmed mean filter, and mood analysis form a mathematical function. The filter iteratively selects different schemes to calculate pixel values at different noise densities with minimum image information. Different processing masks are analyzed to preserve local data at specific image locations correctly in high density. A robust estimator counts false approximation of pixel values as discontinued, identified, and removed. At the post smoothening process, the filter recovers the misclassification of noise-free pixel and blur effects in the image. The qualitative experiments show satisfactory results in storing the details of the image from any image. The performance of the fusion filter is verified with visual quality and performance analysis matrices such as the image enhancement factor, the similarity indicator and the noise ratio from the peak signal.
关键词：Salt and pepper noise; median filter; statistical reasoning; performance analysis matrices; high-density noise; mood; trimmed median; trimmed mean; peak signal-to-noise ratio; image enhancement factor; structural similarity index
