SAR Image Compression With Inherent Denoising Capability Through Knowledge Distillation

Ziyuan Liu, Shaoping Wang, Yuantao Gu

Abstract: Due to its inherent characteristics, the synthetic aperture radar (SAR) image is mainly corrupted by speckle noise, posing additional challenges to lossy image compression algorithms. Traditional optical image compression techniques lack the ability to distinguish between image details and noise, which increases storage costs and restores images that still contain noise. Inspired by these observations, we optimize image compression algorithms to incorporate denoising capabilities, enabling joint denoising and compression of SAR images. Specifically, we transform the raw speckled images into noise-free bitstreams, allowing the subsequent decompression to produce clean images. To achieve this objective efficiently, we introduce a novel knowledge distillation (KD) strategy that incurs no additional computational cost. Furthermore, this distillation mechanism yields statistically significant performance improvements across various image compression algorithms. Experimental results demonstrate that when evaluated on both synthetic and real-world datasets, the proposed method not only achieves the best visual effects but also outperforms existing methods in terms of rate-distortion performance, equivalent number of looks, and other quantitative indicators.