mlsanityData to 14 Oct 2026

Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

arXiv 1703.07478 ↗First posted 22 Mar 2017cs.CVcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Mar 2017.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 4; 2024: 6; 2025: 0; 2026: 2.

15 papers.

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