Image Filtering Using PCNN Pulse-Coupled Neural Networks

This paper presents an image filtering method utilizing PCNN (Pulse-Coupled Neural Networks). The approach employs PCNN to accurately localize noise within images, enabling targeted filtering based on the identified noise distribution. The implementation typically involves configuring PCNN parameters such as linking strength, decay coefficients, and threshold values to optimize noise detection. Once noise regions are identified, adaptive filtering algorithms are applied specifically to these areas, preserving image details while effectively removing noise. This method significantly enhances image quality by maintaining structural integrity during denoising. For practical applications, developers can fine-tune PCNN's pulse-coupled mechanism and adjust filtering parameters according to specific image characteristics and noise types. The algorithm workflow generally includes image preprocessing, PCNN-based noise mapping, region-specific filtering operations, and quality assessment of output images. Experimental implementations often demonstrate improved peak signal-to-noise ratios (PSNR) compared to conventional filtering techniques.