This article proposes a hybrid method for speckle noise reduction in radar images based on a combination of the wavelet transform and the U-Net neural network (NN) architecture with enhancement of low-frequency components in high-frequency subbands. The wavelet transform decomposes the radar images into frequency subbands, allowing noise to be localized primarily in high-frequency components. These components are processed using a U-Net neural network, whose effectiveness stems from its symmetric structure and skip connections, which allow for the accurate preservation and restoration of important image details. Furthermore, enhancing the low-frequency component in high-frequency subbands to improve the signal-to-noise ratio allows the neural network to more accurately separate useful signal structures from the noise. The combined approach demonstrates high speckle noise reduction efficiency with minimal loss of structural information, outperforming traditional methods in terms of restoration quality and image clarity.

Keywords: speckle noise, noise reduction, wavelet transform, neural networks, U-Net, neural networks, frequency subbands