A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting

This paper presents a hybrid SC-NN architecture for effective image inpainting, combining SNNs and CNNs. The model, which includes SNNConv2d layers, outperforms
state-of-the-art approaches by decreasing reconstruction mistakes with lower loss values. The effectiveness indicates a wide range of applications in image regeneration assignments. Combining SNNConv2d with regular CNN layers takes advantage of both SNN and CNN strengths. Future plans include refining the model, investigating applications, and solving real-world problems. The findings highlight SNN’s potential for enhancing artificial intelligence.