Energy-Efficient Median Filter Core Architecture for Impulse Noise Removal in Smart Measurement Systems

In modern measurement systems, preserving accurate data integrity is essential for reliable and precise measurements. The presence of impulse noise in real-world image data can severely degrade the performance of vision algorithms. Median filtering has proven effective in attenuating impulse noise while retaining edge details. Nevertheless, traditional Median Filter architectures often exhibit high power consumption and hardware resource utilization, limiting their practical applicability in resource-constrained applications. Median filtering has proven effective for impulse noise removal while retaining critical measurement features. Nonetheless, existing architectures for Median Filters may suffer from high power consumption and hardware resource utilization, limiting their suitability for smart measurement systems.

To address these limitations, we propose an energy-efficient Median Filter architecture tailored for impulse noise removal in smart measurement systems. The proposed design leverages novel Accumulation of Parallel Computing techniques to minimize pixel movements during the filtering process, leading to significant power savings. By utilizing parallel ring counters, selected pixels are kept stationary in specified cells, enabling efficient and rapid median computation.

The proposed architecture is implemented using HDL Verilog and thoroughly evaluated on the ZYNQ FPGA platform. Through extensive simulation and synthesis, we assess the architecture’s power consumption and processing speed. The results demonstrate a remarkable 60% reduction in power dissipation, 52% reduction in area and a 2X increase in processing speed compared to existing architectures, making the design well-suited for power-constrained measurement applications.

The proposed energy-efficient Median Filter architecture offers an effective and reliable solution for impulse noise removal in smart measurement systems. The design’s optimized power consumption and enhanced processing speed make it highly suitable for real-time and energy-efficient measurements, ensuring the integrity and accuracy of data in a range of measurement applications.