A DEEP LEARNING-BASED METHOD FOR BLUR IMAGE CLASSIFICATION USING DENSENET-121 ARCHITECTURE

Abstract

Blur image classification is essential for computer vision applications, including image quality assessment, surveillance and medical imaging systems. This study proposes a method to classify different types of blur: sharp, Gaussian blur, motion blur, and defocus blur, using the DenseNet-121 architecture. The approach leverages densely connected convolutional layers of DenseNet-121 for efficient, multi-scale feature extraction critical for distinguishing blur types. Data augmentation was applied to create diverse blur patterns, and the model was fine-tuned on a specialized dataset for robust performance. Transition layers and a global average pooling layer with a softmax classifier were incorporated to optimize feature management and output class probabilities. Experiments demonstrated that this method achieves a high accuracy rate of 97.8%, outperforming baseline models in blur classification. Overall, the DenseNet-121-based approach significantly enhanced classification accuracy and provides a scalable, effective solution for real-world image processing tasks that required precise blur detection.