• Nishanth
• Karan Kumar
• Krithi
• Sayeel Shetty
• Rakesh Sharma

Blur detection plays a crucial role in maintaining image clarity across domains like photography, medical imaging, and surveillance. This project introduces an automated blur detection approach using Python and OpenCV. The technique begins by converting the input image to grayscale, followed by applying the Laplacian operator to highlight intensity shifts. The variance of the Laplacian is then computed to assess sharpness, with higher values indicating a clearer image. A predefined threshold helps categorize the image as either sharp or blurry. Additionally, a sharpness percentage is calculated to offer more precise feedback. This method is lightweight and suitable for real-time applications like document scanning and clinical imaging. By embedding this solution into automated systems, only high-quality visuals can be ensured for further analysis.

BLUR DETECTION IN IMAGES USING MACHINE LEARNING

"BLUR DETECTION IN IMAGES USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.46-50, July-2025, Available :http://www.jetir.org/papers/JETIRGX06009.pdf

"BLUR DETECTION IN IMAGES USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp46-50, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06009.pdf