# Blind Image Deblurring with FFT-ReLU Sparsity Prior

arXiv: https://arxiv.org/abs/2406.08344 · HTML: https://mlsanity.com/paper/2406.08344/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text](https://mlsanity.com/paper/2105.05486/) (2021)
- [Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal](https://mlsanity.com/paper/1503.00593/) (2015)
- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)
- [Graph-Based Blind Image Deblurring From a Single Photograph](https://mlsanity.com/paper/1802.07929/) (2018)
- [Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring](https://mlsanity.com/paper/1811.10185/) (2018)
- [Learning a Discriminative Prior for Blind Image Deblurring](https://mlsanity.com/paper/1803.03363/) (2018)
- [Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior](https://mlsanity.com/paper/1906.04442/) (2019)
- [Blind image deblurring using class-adapted image priors](https://mlsanity.com/paper/1709.01710/) (2017)
- [A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring](https://mlsanity.com/paper/1906.06642/) (2019)

## Built on by (most cited first, 2 of 2)

- [Self-Diffusion Driven Blind Imaging](https://mlsanity.com/paper/2510.27439/) (2025)
- [From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring](https://mlsanity.com/paper/2511.10806/) (2025)

## Abstract (arXiv)

Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. It is a small data problem, since the key challenge lies in estimating the unknown degrees of blur from a single image or limited data, instead of learning from large datasets. The solution depends heavily on developing algorithms that effectively model the image degradation process. We introduce a method that leverages a prior which targets the blur kernel to achieve effective deblurring across a wide range of image types. In our extensive empirical analysis, our algorithm achieves results that are competitive with the state-of-the-art blind image deblurring algorithms, and it offers up to two times faster inference, making it a highly efficient solution.