# Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images

## TL;DR

This paper introduces a joint deep learning framework that simultaneously enhances low-resolution, degraded images and segments cracks, improving accuracy in realistic scenarios with unknown image degradations.

## Contribution

It presents a novel end-to-end joint learning approach for blind super-resolution and crack segmentation, including new network paths for mutual optimization.

## Key findings

- Outperforms state-of-the-art segmentation methods
- Effective in real-world degraded image scenarios
- Ablation studies confirm the benefits of joint training

## Abstract

This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks. In the proposed method, a SR network is jointly trained with a binary segmentation network in an end-to-end manner. This joint learning allows the SR network to be optimized for improving segmentation results. For realistic scenarios, the SR network is extended from non-blind to blind for processing a low-resolution image degraded by unknown blurs. The joint network is improved by our proposed two extra paths that further encourage the mutual optimization between SR and segmentation. Comparative experiments with State of The Art (SoTA) segmentation methods demonstrate the superiority of our joint learning, and various ablation studies prove the effects of our contributions.