Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart. We aim to address this by introducing Super-Resolution Generator (SuRGe), a fully-convolutional Generative Adversarial Network (GAN)-based architecture for SR. We show that distinct convolutional features obtained at increasing depths of a GAN generator can be optimally combined by a set of learnable convex weights to improve the quality of generated SR samples. In the process, we employ the Jensen-Shannon and the Gromov-Wasserstein losses respectively between the SR-HR and LR-SR pairs of distributions to further aid the generator of SuRGe to better exploit the available information in an attempt to improve SR. Moreover, we train the discriminator of SuRGe with the Wasserstein loss with gradient penalty, to primarily prevent mode collapse. The proposed SuRGe, as an end-to-end GAN workflow tailor-made for super-resolution, offers improved performance while maintaining low inference time. The efficacy of SuRGe is substantiated by its superior performance compared to 18 state-of-the-art contenders on 10 benchmark datasets.

翻译：超分辨率（SR）是一个经典图像处理问题，旨在将低分辨率（LR）样本的质量提升至其高分辨率（HR）对应样本的标准。我们通过引入基于全卷积生成对抗网络（GAN）的超分辨率生成器（SuRGe）架构来解决这一问题。研究表明，通过一组可学习的凸权重，可以最优地组合GAN生成器在逐层加深过程中获得的不同卷积特征，从而提高生成SR样本的质量。在此过程中，我们分别在SR-HR和LR-SR分布对之间采用Jensen-Shannon散度和Gromov-Wasserstein损失，进一步辅助SuRGe的生成器更好地利用可用信息以改善SR效果。此外，我们使用带梯度惩罚的Wasserstein损失训练SuRGe的判别器，主要目的是防止模式崩塌。作为专为超分辨率定制的端到端GAN工作流，所提出的SuRGe在保持低推理时间的同时实现了更优性能。SuRGe在10个基准数据集上与18种最新竞品相比的优越表现充分验证了其有效性。