FCIR: Rethink Aerial Image Super Resolution with Fourier Analysis

Abstract

Recent years, deep-learning-based methods achieve remarkable improvements on the super-resolution (SR) task. However, recovering high-quality (HQ) texture from the low-quality (LQ) aerial image is still challenging due to the limited contextual modeling ability of current deep-learning methods as well as the sharp artificial texture of aerial images. In this paper, we rethink aerial image super resolution (AISR) task with the perspective of Fourier analysis. Firstly, we build the Fourier Global Convolution (FGC) inspired by the convolution theorem of the Fourier Transform to extract the shadow features. Then, following the Gabor Transform, a carefully designed oriented Texture Contextual Block (OTCB) is proposed to enhance the oriented texture representation. By stacking FGC and OTCB, we propose a simple but effective straight-forward network named Fourier Consistency Image Reconstruction Model (FCIR) to restore HQ aerial image. Moreover, we design a gradient consistency loss (GC Loss) to enhance the quality of reconstructed high-frequency details. Compared with very recent state-of-the-art super-resolution methods, experimental results demonstrate promising SR performance boosts from FCIR on 3 typical aerial image datasets.