A Comprehensive Study of CNN Training Techniques for Image Processing

Abstract

Convolutional Neural Networks (CNNs) have emerged as the backbone of modern image processing and computer vision applications. Despite remarkable progress in CNN architectures, their performance strongly depends on effective training strategies, including weight initialization, optimization algorithms, learning rate scheduling, data augmentation, and regularization techniques. This paper presents a comprehensive study of CNN training methodologies for image processing, integrating theoretical insights, mathematical formulations, and extensive experimental evaluations. We analyze key factors affecting CNN convergence, robustness, and generalization.comparative experiments on CIFAR-10 and a subset of ImageNet demonstrate the impact of different training approaches. Results highlight that optimized training strategies improve classification accuracy, accelerate convergence, and enhance robustness against overfitting. This study serves as a reference framework for researchers and practitioners seeking best practices in CNN-based image processing.