Amplitude, Phase, and Gradient Recovery From Compressed SAR Images

Compressing Synthetic Aperture Radar (SAR) images presents unique challenges due to the high dynamic range and inherent acquisition noise in the amplitude signal, as well as the noise-sensitive and limited information content in the phase signal. Traditional compression methods, such as JPEG and JPEG2000, although widely used, often fail to preserve SAR image quality due to their susceptibility to compression artifacts. The continuous capture of high-resolution raw SAR images over extended periods on drones and Unmanned Aerial Vehicles (UAVs), combined with constraints on computational resources, bandwidth, and onboard storage, further complicates the problem. An effective and efficient compression pipeline is essential for either onboard storage or real-time transmission to ground stations. In this work, we propose a hybrid solution for complex-valued SAR image compression by utilizing the Versatile Video Coding (VVC) framework as a backbone compression engine and employing a deep learning-based method that operates jointly in the pixel and transform domains for deblocking and reconstructing SAR amplitude and phase images. Specifically, we design task-specific compression artifact removal networks calledAmpResandAngResfor amplitude and phase reconstruction, respectively. Additionally, we introduce theGradResnetwork to learn gradients for SAR Scale-Invariant Feature Transform (SAR-SIFT), resulting in robust orientation and magnitude estimations that improve downstream tasks such as keypoints detection and matching in noisy and compressed scenarios. Experimental results demonstrate that our approach achieves a 10% Bjøntegaard Delta (BD)-Rate savings over VVC for amplitude recovery, along with notable improvement in phase reconstruction, and delivers an average of 34% improvement in SAR-SIFT repeatability.