Deep Learning-Based Compression Artifact Removal and Super Resolution for Aerial Imagery
High-resolution aerial imagery plays a significant role in many fields such as urban planning, environmental monitoring, weather prediction, disaster management, change detection and map generation. However, acquiring high-resolution data is often limited by sensor capacities and cost constraints. Moreover, in aerial remote sensing platforms, raw images are compressed during downlink transmission to reduce bandwidth requirements, energy consumption and storage capacity. Lossy image compression algorithms including JPEG degrade image quality, causing artifacts such as blurring, blocking and ringing. In this work, we propose a two-stage framework that employs FBCNN for artifact removal and ESRGAN for super-resolution reconstruction in aerial imagery. We construct our test set based on the SODA-A dataset. Experimental results show that FBCNN achieves high PSNR, SSIM and PSNR-B values across different JPEG quality factors, with PSNR ranging from 28.63 dB to 35.09 dB, SSIM from 0.769 to 0.930, and PSNR-B from 28.40 dB to 34.23 dB. Building upon the outputs of FBCNN, ESRGAN further enhances perceptual quality while maintaining strong quantitative performance, achieving PSNR values from 25.30 dB to 27.99 dB and SSIM values from 0.610 to 0.696 across different quality factors.