A Novel Image Blurring Detection Scheme using Spatial Autocorrelation
Tzong-Jer Chen · 2023
Image blurring prediction has a significant role in image restoration, forensics, and computer vision. Because of both the sharpened blurred image and the original image enlarge the image noise differently. To discern this difference, an autocorrelation measurement is suggested. This paper proposes a non-reference image blurring detection scheme based on the specificity of Moran statistics and UM (Unsharp Masking). Unsharp Masking is sensitive to image noise and extends noise in a sharpening process. The Moran’s Z histogram has been successfully used to discern blurriness and sharpness in an image. The blurred image can be distinguished from images using an examination method that uses the Moran’s Z score histogram on a UM pre-processed image. The Z histogram median values for blurred images are all higher than those from the original image, having a value of approximately 9. Based on these effects, image blur judgment factors are constructed. The features found in this work based on Moran statistics can be in the future fed into a support vector machine (SVM) classifier for classification.