Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Properties of Quadratic Feature Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Primitive Features by Steering, Quadrature, and Scale
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new wavelet-based measure of image focus
Pattern Recognition Letters
Scale-adaptive detection and local characterization of edges based on wavelet transform
Signal Processing - Signal processing in communications
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
IEEE Transactions on Image Processing
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
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Blur estimation is required in image processing techniques such as auto-focussing, quality assessment for compressed images and image fusion. In this paper a multi-scale local blur estimation method is proposed. We use the energy of a pair of quadrature filters with first and second derivatives of a Gaussian at several scales as its constituents. A new strategy for analyzing the extrema of energy across scale is proposed. Comparing to the methods which use just a Gaussian first derivative kernel, a smaller number of scales needs to be processed. Also our method yields only one response at the centroid of line-shape features at a position independent of the scale. This is in contrast to other methods which yield multiple responses at scale dependent positions. We evaluated the method for synthetic and real images from the LIVE database. Depending on details of the image, the proposed method is several to tens of times faster in comparison with using just a Gaussian first derivative. The accuracy of the blur estimation is found to be the best or second best while comparing with 16 different methods for Gaussian blur. In addition, the performance is still good for motion blurred images.