Image Quality Assessment for Gaussian Blur using Siamese Network combined with ResNet-18

2025-08-12T08:05:54+00:00

This paper presents a novel Image Quality Assessment (IQA) framework, SNR (Siamese Network with ResNet-18), specifically designed for Gaussian blur detection. The approach leverages a Siamese network architecture combined with the ResNet-18 backbone to process image pairs—one blurred and one reference—to predict image quality based on their differences. The model effectively captures high-frequency features lost due to blur, such as edges and gradients. We conduct extensive experiments on the TID2013 dataset, showing that SNR achieves superior performance in blur-specific IQA tasks compared to other full-reference methods. Data augmentation techniques significantly improve model generalization, resulting in a test accuracy of 97.37% for ResNet-18. The proposed method demonstrates a strong correlation with human judgment and robust generalization across various image contents, with future work focusing on expanding its applicability to other distortions and optimizing computational efficiency.

Beyond the black box: How fuzzy logic and multi-modal AI are revolutionizing personalized education

2025-07-24T02:53:47+00:00

In classrooms around the world, educators are drowning in data—but starving for insight. Quiz scores, video engagement, homework submissions, and login timestamps: all are logged, analyzed, and visualized. Yet these numbers rarely answer the deeper questions. Why is a student struggling? What kind of support do they need? Most educational AI systems treat data points as isolated facts, ignoring the tangled web of factors that shape learning.

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Image Quality Assessment for Gaussian Blur using Siamese Network combined with ResNet-18

Beyond the black box: How fuzzy logic and multi-modal AI are revolutionizing personalized education