# SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network

arXiv: https://arxiv.org/abs/2206.12930 · HTML: https://mlsanity.com/paper/2206.12930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [NTIRE 2021 Challenge on Image Deblurring](https://mlsanity.com/paper/2104.14854/) (2021)
- [Deep Multi-Scale Feature Learning for Defocus Blur Estimation](https://mlsanity.com/paper/2009.11939/) (2020)

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

- [A Dictionary Based Approach for Removing Out-of-Focus Blur](https://mlsanity.com/paper/2406.11330/) (2024)
- [ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring](https://mlsanity.com/paper/2606.06540/) (2026)

## Abstract (arXiv)

Defocus blur is a physical consequence of the optical sensors used in most cameras. Although it can be used as a photographic style, it is commonly viewed as an image degradation modeled as the convolution of a sharp image with a spatially-varying blur kernel. Motivated by the advance of blur estimation methods in the past years, we propose a non-blind approach for image deblurring that can deal with spatially-varying kernels. We introduce two encoder-decoder sub-networks that are fed with the blurry image and the estimated blur map, respectively, and produce as output the deblurred (deconvolved) image. Each sub-network presents several skip connections that allow data propagation from layers spread apart, and also inter-subnetwork skip connections that ease the communication between the modules. The network is trained with synthetically blur kernels that are augmented to emulate blur maps produced by existing blur estimation methods, and our experimental results show that our method works well when combined with a variety of blur estimation methods.