This paper presents an enhanced method for noise removal and target detection in Synthetic Aperture Radar (SAR) images using a Denoising Convolutional Neural Network (DnCNN) combined with wavelet transform. Unlike conventional method, the proposed framework focuses on remove the Speckle Noise through residual learning and wavelet transform. The DnCNN architecture, consisting of 29 layers, efficiently removes noise while preserving high-frequency image features. The integration of wavelet transform further enhances noise removal and feature preservation. Experimental results demonstrate that the recognition rate of the proposed method improves by about 94% compared to original method under 10 dB Speckle Noise conditions. This method outperforms conventional algorithm in SAR image processing, making it highly suitable for applications in noisy environments.
