# Blind Deblurring using Deep Learning: A Survey

arXiv: https://arxiv.org/abs/1907.10128 · HTML: https://mlsanity.com/paper/1907.10128/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 7 of 7)

- [TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2404.16306/) (2024)
- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution](https://mlsanity.com/paper/2304.03542/) (2023)
- [A Comprehensive Survey on Deep Neural Image Deblurring](https://mlsanity.com/paper/2310.04719/) (2023)
- [Enhancing variational generation through self-decomposition](https://mlsanity.com/paper/2202.02738/) (2022)
- [ReBotNet: Fast Real-time Video Enhancement](https://mlsanity.com/paper/2303.13504/) (2023)
- [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023)

## Abstract (arXiv)

We inspect all the deep learning based solutions and provide holistic understanding of various architectures that have evolved over the past few years to solve blind deblurring. The introductory work used deep learning to estimate some features of the blur kernel and then moved onto predicting the blur kernel entirely, which converts the problem into non-blind deblurring. The recent state of the art techniques are end to end, i.e., they don't estimate the blur kernel rather try to estimate the latent sharp image directly from the blurred image. The benchmarking PSNR and SSIM values on standard datasets of GOPRO and Kohler using various architectures are also provided.