Efficient Parallel Median Filter for Image Denoising: Implementation and Performance Evaluation
Mahendrakan Kantharimuthu, Prema Selvaraj, Harish Sankar, Gokulavasan Brindavanam · Traitement du signal · 2024
The sorting network forms the foundation of the suggested parallel median filters and the cutting-edge filter produced noise-free images.To enhance such filters noise-elimination abilities, a particular comparator is created.Signal leaps can be preserved while noise is reduced with the help of parallel median filtering.The noise elimination determines how much noise is reduced.The filter does a better job of minimizing noise the heavier the distribution tail.Median filtering preserves edge signal, which is a crucial aspect of images, more effectively than average filtering.New median filters have a consistent, modular architecture.Limited precision computations are allowed in applications that process audio and images.The approximate computing can be implemented in the digital system with sufficient precision.This paper proposes a novel technique for the low-cost area, power and speed-efficient manufacturing of 2-bit magnitude comparators.The new technology created larger comparators with tunable error characteristics.Further, parallel median filter is designed with additional 2 ternary data sorter for high speed application which processes the data in parallel.From Simulation results, the proposed filter achieves more power, area and speed.The filters output value is essentially equivalent to the particular one when it comes to filtering precision and circuit features.When compared to serial median filters, parallel median filters dynamic power consumption is 36.37%higher and also total estimated power consumption of parallel median filter is 30.80% more compare to with serial Median Filter.In logic distribution, 5% of number of occupied slices is reduced in the parallel median filter.Parallel design uses 26.76% fewer total equivalent gates than serial design.Simulations show that inexact filter implementations can save up to 30% and 26% of energy and space, respectively, and can accelerate operations by 15% when compared to standard accurate ones.