ESTUGAN: Enhanced Swin Transformer with U-Net Discriminator for Remote Sensing Image Super-Resolution

Abstract

Remote sensing image super-resolution (SR) is a practical research topic with broad applications. However, the mainstream algorithms for this task suffer from limitations. CNN-based algorithms face difficulties in modeling long-term dependencies, while generative adversarial networks (GANs) are prone to producing artifacts, making it difficult to reconstruct high-quality, detailed images. To address these challenges, we propose ESTUGAN for remote sensing image SR. On the one hand, ESTUGAN adopts the Swin Transformer as the network backbone and upgrades it to fully mobilize input information for global interaction, achieving impressive performance with fewer parameters. On the other hand, we employ a U-Net discriminator with the region-aware learning strategy for assisted supervision. The U-shaped design enables us to obtain structural information at each hierarchy and provides dense pixel-by-pixel feedback on the predicted images. Combined with the region-aware learning strategy, our U-Net discriminator can perform adversarial learning only for texture-rich regions, effectively suppressing artifacts. To achieve flexible supervision for the estimation, we employ the Best-buddy loss. And we also add the Back-projection loss as a constraint for the faithful reconstruction of the high-resolution image distribution. Extensive experiments demonstrate the superior perceptual quality and reliability of our proposed ESTUGAN in reconstructing remote sensing images.

Learning the Frequency Domain Aliasing for Real-World Super-Resolution

“…Single-image super-resolution (SR) aims to enhance the quality of low-resolution images by reconstructing them into high-resolution counterparts. Learning-based methods [1][2][3][4][5][6][7][8][9][10], such as SRCNN [1], VDSR [2], LapSRN [3], RCAN [4], SRGAN [5], and ESRGAN [6], have made significant advancements in achieving impressive results. Typically, these methods require pairs of high-resolution (HR) and low-resolution (LR) images for training.…”