DPON: A Novel Deep Learning Framework for Efficient Image Processing

Abstract

The Deep Processing Optimization Network (DPON) is a novel deep learning framework designed to enhance image processing and classification tasks. By incorporating advanced techniques such as convolutional layers, residual connections, attention mechanisms, and global average pooling, DPON optimizes both performance and computational efficiency. The architecture is carefully structured to address key limitations in traditional convolutional neural networks (CNNs), particularly in terms of feature extraction and gradient flow, while maintaining a balance between accuracy and processing speed. Through comprehensive evaluations on benchmark datasets, including CIFAR-10, DPON demonstrates superior performance across multiple metrics, including accuracy, precision, recall, and F1-score, consistently outperforming established models such as ResNet-50, EfficientNet-B0, and DenseNet-121. The integration of attention mechanisms allows DPON to focus on the most relevant features of the input, improving classification precision while reducing misclassification rates. Additionally, the use of global average pooling significantly reduces the number of parameters, enhancing computational efficiency without sacrificing accuracy. DPON's robust design makes it highly adaptable for a wide range of applications, from image classification to real-time systems requiring fast inference times. This paper presents the architectural details of DPON, its mathematical foundation, and extensive performance evaluations, demonstrating its potential as a state-of-the-art solution for modern image processing challenges.