Blurred Image Regions Research Articles

A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection

Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods.

Features Extraction for Detection of Blurred Image Regions

ABSTRACTThe article discusses our study of the problem of blurred image regions detection. Proposed are 21 features: 20 based on the discrete wavelet transform and one being a ratio based on a gray-level co-occurrence matrix. Described features are introduced in order to detect blurred segment regardless of the blur type such as background blur, motion blur, or out of focus. Moreover, proposed features are relatively easy to calculate and parallelize. Multilayer perceptron with a backpropagation algorithm is trained on mentioned features. Three approaches are introduced and tested: (1) with nonoverlapping fixed size windows, (2) with nonoverlapping recursively divided windows, and (3) with addition of a morphological closing operator.