Quality-aware pixel-level fusion for robust SAR image despeckling
Emrah Onat · International Journal of Image and Data Fusion · 2026
Synthetic Aperture Radar (SAR) images are inherently affected by speckle noise, which degrades image quality and complicates subsequent analysis. In this study, a novel metric-driven pixel-level fusion framework is proposed that combines multiple despeckling algorithms to generate a single high-quality despeckled SAR image. For each despeckled result, quality maps are computed using both full-reference metrics, such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Universal Quality Index (UQI), as well as no-reference metrics, including entropy, Edge Preservation Index (EPI) and Mean of Ratio (MoR). To address the absence of clean ground-truth images in real SAR applications, the revised framework also incorporates pseudo-reference image generation strategies inspired by recent encoder–decoder and optical-image-assisted SAR restoration approaches. At each pixel location, the algorithm selects the pixel value corresponding to the optimal local quality score, thereby constructing a fused image with enhanced structural preservation and speckle suppression. Experimental results obtained from both simulated and real SAR datasets demonstrate that the proposed method consistently outperforms individual despeckling algorithms in terms of perceptual and statistical quality measures. In addition, the integration of recent state-of-the-art despeckling methods further improves the robustness and flexibility of the proposed fusion framework for practical remote sensing applications.