# Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks

## TL;DR

This paper introduces a multi-stream neural network that leverages semantic face labels and confidence measures to improve the quality of deblurred face images, outperforming existing methods.

## Contribution

The novel Uncertainty Guided Multi-Stream Semantic Network (UMSN) utilizes semantic segmentation and confidence-guided training for enhanced face deblurring.

## Key findings

- Significant improvements over state-of-the-art face deblurring methods.
- Effective handling of challenging facial regions like eyes and nose.
- End-to-end training framework for semantic-guided deblurring.

## Abstract

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi- Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end- to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: https://github.com/ rajeevyasarla/UMSN-Face-Deblurring