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

arXiv: https://arxiv.org/abs/1703.07478 · HTML: https://mlsanity.com/paper/1703.07478/

## Key facts

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

## Built on by (most cited first, 15 of 15)

- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field](https://mlsanity.com/paper/2406.07329/) (2024)
- [Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition](https://mlsanity.com/paper/2102.01026/) (2021)
- [Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks](https://mlsanity.com/paper/2308.02947/) (2023)
- [MatchDiffusion: Training-free Generation of Match-cuts](https://mlsanity.com/paper/2411.18677/) (2024)
- [Depth and DOF Cues Make A Better Defocus Blur Detector](https://mlsanity.com/paper/2306.11334/) (2023)
- [Optical Lens Attack on Deep Learning Based Monocular Depth Estimation](https://mlsanity.com/paper/2409.17376/) (2024)
- [Motion Segmentation for Neuromorphic Aerial Surveillance](https://mlsanity.com/paper/2405.15209/) (2024)
- [Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid](https://mlsanity.com/paper/2406.05349/) (2024)
- [Leveraging Local Domains for Image-to-Image Translation](https://mlsanity.com/paper/2109.04468/) (2021)
- [Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving](https://mlsanity.com/paper/2411.00192/) (2024)
- [EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome](https://mlsanity.com/paper/2205.06765/) (2022)
- [A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure](https://mlsanity.com/paper/2311.12845/) (2023)
- [Subtle Motion Blur Detection and Segmentation from Static Image Artworks](https://mlsanity.com/paper/2602.18720/) (2026)
- [BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet](https://mlsanity.com/paper/2607.10427/) (2026)

## 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.