LAUSR

Abstract Recent semantic segmentation algorithms are greatly accelerated by deep convolutional neural networks (DCNNs). Although most of them perform well on normal images, they are not robust to the degenerations of images. To boost the performance of semantic segmentation on degraded images, we present an effective image restoration framework based on generative adversarial network (GAN). Firstly, we propose to jointly minimize pixel-wise cross entropy loss and our semantic-aware mean square error loss for the optimization of generative restoration network, which ensures correct semantic predictions at each pixel location. Secondly, we introduce GAN into our framework, which can benefit high-order consistency of semantic predictions. Comprehensive experimental results on image super-resolution and denoising demonstrate that our approach is able to achieve the best segmentation accuracy as well as maintaining pleasing image quality compared to other common image restoration approaches.