Image Fusion Based on PCNN (Pulse-Coupled Neural Network) with Algorithm Implementation

PCNN-based image fusion is a technique that integrates biological vision mechanisms with digital image processing. The Pulse-Coupled Neural Network (PCNN) mimics the working principles of neurons in the mammalian visual cortex, achieving image feature extraction and fusion through pulse synchronization characteristics. In implementation, PCNN models typically require setting parameters like linking strength (β), decay constants, and threshold values, which can be programmed using matrix operations for efficient computation.

The PCNN image fusion process generally involves three key stages: First, multi-scale decomposition (such as wavelet or pyramid transforms) breaks down source images into different frequency bands. Then, the PCNN network selectively fuses coefficients from each decomposed layer using pulse-coupled mechanisms - where neurons with similar input stimuli fire simultaneously. Finally, the fused image is reconstructed through inverse transformation. Critical parameters like linking strength and iteration count directly impact fusion quality, requiring careful tuning through experimental validation or optimization algorithms.

Evaluation metrics typically consider multiple dimensions: Information entropy reflects image information richness, spatial frequency assesses clarity, and mutual information measures the correlation between source images and fusion results. Compared to traditional fusion methods, PCNN better preserves edge and texture features, showing advantages in medical imaging and remote sensing applications. Recent research trends include combining deep learning to optimize PCNN parameters automatically and improving multi-scale decomposition methods to enhance fusion performance. Code implementation often involves parallel processing for PCNN iterations and weighted fusion rules for coefficient combination.