Gaussian Blur Image Restoration Method Using BP Neural Network

This research presents a Gaussian blur image restoration method based on Backpropagation (BP) neural network. By utilizing the nonlinear approximation capability of BP neural networks, the method effectively restores Gaussian-blurred images. Compared to traditional algorithms, this approach demonstrates superior restoration quality and performance. The nonlinear approximation function of BP neural networks enables more accurate and refined image restoration processes, allowing for better recovery of image details and sharpness. The implementation typically involves several key components: - Network architecture design with appropriate hidden layers and neuron counts - Preparation of training datasets containing paired blurred and clear images - Loss function optimization using mean squared error or perceptual loss metrics - Backpropagation algorithm implementation for weight updates - Activation functions like sigmoid or ReLU for nonlinear transformations Therefore, this research method holds significant importance in the field of Gaussian blur image restoration and can provide substantial support and guidance for research and applications in image restoration domains. The code structure generally follows standard neural network implementation patterns while incorporating domain-specific preprocessing and postprocessing steps for image data.