Neural Network Backpropagation Training for Pixel Value-Temperature Mapping in Infrared Images

This program, implemented in MATLAB, applies the neural network backpropagation (BP) method to train the mapping relationship between infrared image pixel values and their corresponding temperature values. The primary objective is to establish a predictive model that can accurately infer temperature distributions from infrared image data through supervised learning. The implementation utilizes MATLAB's Neural Network Toolbox, where key functions like `feedforwardnet` or `patternnet` are configured with backpropagation algorithm for weight optimization. The training process involves feeding normalized pixel intensity values as input features and corresponding temperature measurements as target outputs, with hidden layers employing sigmoid or ReLU activation functions. This approach enables deeper understanding of thermal patterns in infrared imagery and provides a foundation for temperature prediction based solely on pixel values. The program offers significant potential for advancing infrared image processing and analytical capabilities, particularly in thermal monitoring and non-destructive testing applications.