Analysis of blur measure operators for single image blur segmentation

Abstract

Blur detection and segmentation for a single image without any prior information is a challenging task. Numerous techniques for blur detection and segmentation have been proposed in the literature to ultimately restore the sharp images. These techniques use different blur measures in different settings, and in all of them, blur measure plays a central role among all other steps. Blur measure operators have not been analyzed comparatively for both of the spatially-variant defocus and motion blur cases. In this paper, we provide the performance analysis of the state-of-the-art blur measure operators under a unified framework for blur segmentation. A large number of blur measure operators are considered for applying on a diverse set of real blurry images affected by different types and levels of blur and noise. The initial blur maps then are segmented into blurred and non-blurred regions. In order to test the performance of blur measure operators in segmentation process in equal terms, it is crucial to consider the same intermediate steps involved in the blur segmentation process for all of the blur measure operators. The performance of the operators is evaluated by using various qualitative measures. Results reveal that the blur measure operators perform well under certain condition and factors. However, it has been observed that some operators perform adequately overall well or worse against almost all imperfections that prevail over the real-world images.