Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes
This addresses the challenge of blur detection in image processing for applications like computer vision, but it is incremental as it builds on existing methods with a novel approach.
The paper tackled the problem of detecting spatially-varying blur from a single image without prior knowledge of blur type, level, or camera settings, and the result was that the proposed algorithm performed favorably against state-of-the-art methods in qualitative and quantitative evaluations on diverse blurry images.
The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address the blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.