Linear Gaussian blur evolution for detection of blurry images

Abstract

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.