Stacked Semantically-Guided Learning for Image De-distortion

Huiyuan Fu, Changhao Tian, Xin Wang, Huadong Ma

Abstract: Image de-distortion is very important because distortions will degrade the image quality significantly. It can benefit many computational visual media applications that are primarily designed for high-quality images. In order to address this challenging issue, we propose a stacked semantically-guided network, which is the first try on this task. It can capture and restore the distortions around the humans and the adjacent background effectively with the stacked network architecture and the semantically-guided scheme. In addition, a discriminative restoration loss function is proposed to recover different distorted regions in the images discriminatively. As another important effort, we construct a large-scale dataset for image de-distortion. Extensive qualitative and quantitative experiments show that our proposed method achieves a superior performance compared with the state-of-the-art approaches.