# Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

arXiv: https://arxiv.org/abs/2102.01026 · HTML: https://mlsanity.com/paper/2102.01026/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (2017)
- [DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://mlsanity.com/paper/1908.03826/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [The Little Engine that Could: Regularization by Denoising (RED)](https://mlsanity.com/paper/1611.02862/) (2016)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [Deep Stacked Hierarchical Multi-patch Network for Image Deblurring](https://mlsanity.com/paper/1904.03468/) (2019)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur](https://mlsanity.com/paper/1612.02583/) (2016)
- [Regularization by Denoising via Fixed-Point Projection (RED-PRO)](https://mlsanity.com/paper/2008.00226/) (2020)
- [Basis Prediction Networks for Effective Burst Denoising with Large Kernels](https://mlsanity.com/paper/1912.04421/) (2019)
- [Generalized Video Deblurring for Dynamic Scenes](https://mlsanity.com/paper/1507.02438/) (2015)
- [Reblur2Deblur: Deblurring Videos via Self-Supervised Learning](https://mlsanity.com/paper/1801.05117/) (2018)
- [Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes](https://mlsanity.com/paper/1703.07478/) (2017)
- [Removing Camera Shake via Weighted Fourier Burst Accumulation](https://mlsanity.com/paper/1505.02731/) (2015)
- [Self-supervised blur detection from synthetically blurred scenes](https://mlsanity.com/paper/1908.10638/) (2019)
- [Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks](https://mlsanity.com/paper/1612.01227/) (2016)

## Built on by (most cited first, 10 of 10)

- [Deep Model-Based Super-Resolution with Non-uniform Blur](https://mlsanity.com/paper/2204.10109/) (2022)
- [Event-based Image Deblurring with Dynamic Motion Awareness](https://mlsanity.com/paper/2208.11398/) (2022)
- [NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge](https://mlsanity.com/paper/2405.09923/) (2024)
- [Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization](https://mlsanity.com/paper/2404.12168/) (2024)
- [FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring](https://mlsanity.com/paper/2510.01641/) (2025)
- [Procedural Kernel Networks](https://mlsanity.com/paper/2112.09318/) (2021)
- [Robust Scene Inference under Noise-Blur Dual Corruptions](https://mlsanity.com/paper/2207.11643/) (2022)
- [Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels](https://mlsanity.com/paper/2210.10605/) (2022)
- [ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2606.06540/) (2026)
- [AstraMoE-SR: Trajectory-Guided Diffusion for Blind Satellite Jitter Deblurring and Super-Resolution](https://mlsanity.com/paper/2609.07012/) (2026)

## Abstract (arXiv)

Motion blur estimation remains an important task for scene analysis and image restoration. In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. Characterization of the motion blur, on the other hand, has received less attention, and progress in model-based methods for deblurring lags behind that of data-driven end-to-end approaches. In this work we revisit the problem of characterizing dense, non-uniform motion blur in a single image and propose a general non-parametric model for this task. Given a blurry image, a neural network is trained to estimate a set of image-adaptive basis motion kernels as well as the mixing coefficients at the pixel level, producing a per-pixel motion blur field. We show that our approach overcomes the limitations of existing non-uniform motion blur estimation methods and leads to extremely accurate motion blur kernels. When applied to real motion-blurred images, a variational non-uniform blur removal method fed with the estimated blur kernels produces high-quality restored images. Qualitative and quantitative evaluation shows that these results are competitive or superior to results obtained with existing end-to-end deep learning (DL) based methods, thus bridging the gap between model-based and data-driven approaches.