RT Journal Article
A1 E. Tsomko
A1 H.J. Kim
A1 E. Izquierdo

PB iet
T1 Linear Gaussian blur evolution for detection of blurry images
JN IET Image Processing
VO 4
IS 4
SP 302
OP 312
AB Even though state-of-the-art digital cameras are equipped with auto-focusing and motion compensation functions, several other factors including limited contrast, inappropriate exposure time and improper device handling can still lead to unsatisfactory image quality such as blurriness. Indeed, blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed. A new technique for automatic detection and removal of blurry pictures is presented. Initially, a set of interest points and local image areas is extracted. These areas are then evolved in time according to the conventional linear scale space. The gradient of the evolution curve through scale is then used to produce a ‘blur graph’ representing the probability of a picture being blurred or not. Complexity is kept low by applying a Monte-Carlo like technique for the selection of representative image areas and interest points and by implicitly estimating the gradient of the scale-space curve evolution. An exhaustive evaluation of the proposed technique is conducted to validate its performance in terms of detection accuracy and efficiency.
K1 digital cameras
K1 image extraction
K1 motion compensation functions
K1 autofocusing
K1 conventional linear scale space
K1 blur graph
K1 scale-space curve evolution
K1 Monte-Carlo like technique
K1 image collections
K1 blurry image detection
K1 automatic deletion
K1 linear Gaussian blur evolution
DO https://doi.org/10.1049/iet-ipr.2009.0001
UL https://digital-library.theiet.org/;jsessionid=c3l85imc3krl8.x-iet-live-01content/journals/10.1049/iet-ipr.2009.0001
LA English
SN 1751-9659
YR 2010