Wavelet-Autoencoder Hybrid Model for Enhanced Image Denoising in Medical Imaging


Hewa Majeed Zangana (Duhok Polytechnic University, Iraq) and Firas Mahmood Mustafa (Duhok Polytechnic University, Iraq)

https://www.igi-global.com/chapter/wavelet-autoencoder-hybrid-model-for-enhanced-image-denoising-in-medical-imaging/381166

This chapter proposes a novel hybrid approach that combines the strengths of wavelet transform with the powerful learning capabilities of autoencoder networks to achieve superior denoising performance. By leveraging wavelet decomposition to process images at multiple scales and feeding these decomposed signals into a deep autoencoder network, we effectively suppress noise while maintaining high-frequency details. Extensive experiments demonstrate that our method outperforms existing techniques, yielding significant improvements in both peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results suggest that the integration of wavelet transform and autoencoder networks offers a promising solution for robust image denoising, especially in scenarios with complex noise patterns.