Розробка гібридного методу VGG16-FrostNet для адаптивної деспеклізації зображень радіолокатора із синтезованою апертурою (РСА) із використанням механізму уваги та диференційовного фільтра Фроста

The object of research is the process of suppressing multiplicative speckle noise in synthetic aperture radar (SAR) images, which significantly complicates their analysis. The problem addressed is the lack of end-to-end hybrid methods capable of spatial adaptation by integrating a mathematical model of local statistics (the Frost filter) directly into the neural network computation graph. This research is aimed at automating the process of adaptive SAR image filtering by developing the hybrid VGG16-FrostNet method. These research tasks were addressed by formulating a differentiable mathematical model of the classical Frost filter for integration into a neural network, developing an architecture based on a pretrained VGG16 (Visual Geometry Group) backbone (blocks 1–2), and integrating the Convolutional Block Attention Module (CBAM), which predicts a spatially varying damping coefficient map Amap within 0.5–10.0 for each pixel. The developed hybrid architecture includes a residual branch for detail recovery and was optimized end-to-end using a comprehensive loss function combining L1, Edge Loss (Sobel), SSIM, and attention regularization. The model was trained on synthetic data with gamma-distributed speckle (equivalent looks between 3.0 and 6.0) under typical SAR conditions. On the test set, experimental evaluation yielded a mean PSNR of 34.18 dB and SSIM of 0.97. The gain relative to the noisy image constituted 9.45 dB, and 3.36 dB in PSNR compared to the classical Frost filter with an optimal static coefficient. Edge indicators EPI = 0.8903 and FOM = 0.8340 substantiate reliable preservation of structural boundaries. It was established that the developed hybrid method provides spatially adaptive damping with interpretable attention maps, enabling its deployment in automated SAR data processing pipelines.