Machine Learning for Detecting Blurred Images

This paper presents an innovative method for detecting blurry images by utilizing machine learning techniques. It integrates traditional image quality attributes with deep learning via a convolutional neural network (CNN) to differentiate between sharp and blurry images. The use of a diverse training dataset ensures the model’s robustness and ability to generalize, while data augmentation techniques mitigate the risk of overfitting. Experimental findings on standard datasets confirm the effectiveness of the proposed approach in detecting various types and degrees of blur, including motion blur and defocus blur. This method constitutes a significant advancement in blur image detection, offering a versatile tool with potential for further refinement as machine learning continues to evolve. Its applicability extends to a wide array of real-world scenarios in image analysis and decisionmaking processes.