Exploring the Efficacy of Nonlinear Filters in CMOS for 2-D Signal Processing for Image Quality Enhancement

Area of Science:

• Electrical Engineering
• Computer Vision
• Signal Processing

Background:

• Traditional linear filters struggle with nonlinear data and impulse noise in 2-D signal processing.
• CMOS image sensors require advanced filtering techniques for optimal image quality.

Purpose of the Study:

• To investigate the effectiveness of nonlinear filters in CMOS for 2-D signal processing.
• To compare nonlinear filters, specifically the median-median (Med-Med) filter, against traditional linear filters.
• To address hardware implementation challenges including power consumption and chip area.

Main Methods:

• Comparative analysis of linear and nonlinear filters using metrics like Normalized Squared Error (NSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM).
• Simulation and experimental validation of filter performance under controlled conditions.
• Development and testing of a novel hardware architecture for the Med-Med filter using a 9-pixel image sensor array.

Main Results:

• Nonlinear filters, particularly the Med-Med approach, demonstrate superior performance in removing impulse noise and enhancing image quality compared to linear filters in CMOS.
• The proposed Med-Med filter architecture shows functional validation and potential for hardware implementation.
• Optimized hardware implementation strategies for power consumption and chip area were addressed.

Conclusions:

• Nonlinear filters in CMOS offer a significant advancement for real-time image quality enhancement in 2-D signal processing.
• The findings support the broader applicability of these filters in diverse real-world imaging systems.
• This research bridges theoretical concepts with practical hardware implementation, advancing visual technology.