HARC-Net: Hierarchical Multiaxis Representation and Adaptive Residual Calibration for End-to-End SAR-to-Optical Image Translation

Abstract

Synthetic aperture radar (SAR) enables all-weather Earth observation; however, its inherent multiplicative speckle noise and geometry-dependent distortions pose significant challenges for SAR-to-optical image translation, often leading to structural deformation and degraded texture fidelity. To address these issues, this article presents Hierarchical MultiAxis Representation and Adaptive Residual Calibration Network (HARC-Net), an end-to-end Transformer-based regression framework that combines hierarchical multiaxis representation learning with statistics-guided skip-feature calibration to improve robustness and reconstruction quality. At the core of the proposed approach is a variable-axis sparse transformer (VASTormer) encoder, which integrates convolutional inductive bias with hierarchical multiaxis attention, including local block attention and sparse grid attention. This task-oriented encoder design enables efficient modeling of long-range dependencies while maintaining stable feature representations under speckle perturbations. To mitigate noise propagation in U-shaped architectures, we further introduce an adaptive dual attention and residual calibration (ADARC) module for skip connections. ADARC combines multistatistic spatial pooling (mean, max, min, and sum) with channelwise attention and learnable residual gating, effectively suppressing speckle-sensitive responses and improving semantic alignment between encoder and decoder features. Extensive experiments on two paired benchmarks, SEN1-2 and QXS-SAROPT, demonstrate that HARC-Net consistently achieves superior performance in both reconstruction quality and structural fidelity. The proposed method significantly reduces speckle-induced artifacts while preserving fine geometric details and linear structures. These results highlight the effectiveness of combining hierarchical local–global representation learning with statistics-guided feature calibration for robust cross-modal translation in remote sensing applications requiring geometrically consistent and noise-resilient optical reconstruction under adverse imaging conditions.