An Enhanced Noise Removal-based SAR Image Recognition Using DnCNN and Wavelet Transform

Abstract

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 trans¬form. Unlike conventional method, the proposed frame¬work focuses on remove the Speckle Noise through residu¬al 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 pro¬cessing, making it highly suitable for applications in noisy environments.