Convolutional Neural Network for Blur Images Detection as an Alternative for Laplacian Method

TL;DR

This paper introduces a deep convolutional neural network approach for detecting blurry images, offering an automated alternative to traditional methods like the Laplacian, with demonstrated effectiveness through experimental evaluation.

Contribution

The paper proposes a novel CNN-based method for blur detection, providing an automated and potentially more accurate alternative to existing deterministic techniques.

Findings

CNN outperforms traditional methods in accuracy

High effectiveness demonstrated through confusion matrix analysis

Suitable for real-time image quality assessment

Abstract

With the prevalence of digital cameras, the number of digital images increases quickly, which raises the demand for non-manual image quality assessment. While there are many methods considered useful for detecting blurriness, in this paper we propose and evaluate a new method that uses a deep convolutional neural network, which can determine whether an image is blurry or not. Experimental results demonstrate the effectiveness of the proposed scheme and are compared to deterministic methods using the confusion matrix.