Image and Signal Denoising Methods
Open access
Peer reviewed
Scollr summary
What this paper is about
An improved median filter integrated with adaptive thresholds and local statistical features is proposed, achieving remarkable superiority over conventional median filters across multiple noise densities, verifying its reliability for image denoising tasks.
Full abstract
Read the full abstract
Traditional median filters easily blur fine textures and weaken edge definition during salt-and-pepper noise elimination. To tackle this limitation, this paper proposes an improved median filter integrated with adaptive thresholds and local statistical features. The algorithm firstly classifies the central pixel as an extreme point or ordinary pixel. For extreme pixels, median values and adaptive thresholds are computed only from valid non-extreme pixels inside the filtering window; for non-extreme pixels, dynamic discrimination thresholds are built relying on the global window's median and local deviation. A central pixel will be substituted with the corresponding median once its gray deviation exceeds the adaptive threshold. Simulation results demonstrate that the presented method achieves remarkable superiority over conventional median filters across multiple noise densities, verifying its reliability for image denoising tasks.
Direct answer
What can I do from this paper page?
Use this page to scan "<b>I</b><b>mproved Median Filtering for Image Denoising</b>" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Image and Signal Denoising Methods research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Liu2026mproved,
title = {<b>I</b><b>mproved Median Filtering for Image Denoising</b>},
author = {Yuqing Liu and Jianqiang Gao and Lei Yan and Xinyu Dong and Li Li and Yufeng Wang and Xiu Liu},
journal = {Journal of Computer Science and Technological Enquiry},
year = {2026},
doi = {10.65496/jcste.2026.91},
url = {https://doi.org/10.65496/jcste.2026.91}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Image and Signal Denoising Methods research papers?
Follow Image and Signal Denoising Methods research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for <b>I</b><b>mproved Median Filtering for Image Denoising</b>. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app