Blur detection for digital images using wavelet transform

By Hanghang Tong, Mingjing Li, HongJiang Zhang, Changshui Zhang. IEEE International Conference on Multimedia and Expo, 2004

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dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs (2017, background): “The second is knowledge about degradation, which can then be explicitly taken into account to build features for particular artifacts, such as blocking [22]–[24], blurring [25]–[27] and ringing [28]–[30].”
Assessing the aesthetic quality of photographs using generic image descriptors (2011, methodology): “Other descriptors are based on blur estimation techniques where the blur is modeled with a Gaussian smoothing filter [27].”
The Design of High-Level Features for Photo Quality Assessment (2006, background): “’s blur estimation technique [16] and combine it with our own, which we found to give the best results.”