A Novel Parallelized Modified Decision-Based Median Filtering for Computationally Efficient Image Restoration under Impulse Noise

Satellite image denoising is essential for preserving image quality in remote sensing applications, where impulse noise significantly degrades captured data. To address this challenge, this method proposes an ultra-fast parallelized modified decision-based median filter (PMDBMF). It effectively removes impulse noise while preserving structural details. The proposed approach leverages fixed parallelization to achieve superior noise reduction with minimal computational overhead. Compared to the decision-based median filter (DBMF), the proposed PMDBMF approach achieves an overall improvement of approximately 13 %. This result demonstrates the efficiency of PMDBMF in delivering high-quality noise removal while significantly reducing processing time, making it a promising solution for real-time satellite image processing. Additionally, the PMDBMF maintains fine image details while effectively suppressing impulse noise, ensuring superior structural integrity compared to traditional median-based approaches. Its fixed parallelization strategy enhances scalability across various hardware architectures, enabling real-time deployment in resource-constrained environments. This efficiency is highly significant for research-driven domains such as environmental monitoring, disaster assessment, and geospatial analysis, where rapid and reliable image restoration is essential. Experimental analysis confirmed that the proposed PMDBMF framework achieves superior structural integrity, with robust edge and texture preservation, and enhanced noise suppression, as evidenced by notable improvements in the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index metrics (SSIM), and computational complexity metrics.