Proximal Mapping-Incorporated Deep Autoencoder Network with Momentum Acceleration Method for Image Denoising

In the field of image processing, autoencoder networks have emerged as potent approaches for image denoising. However, traditional autoencoder networks often struggle with imprecise noise modeling and slower training convergence, which hinder their denoising performance. To address these limitations, we propose a novel image denoising autoencoder network incorporated with the proximal mapping operator and the momentum acceleration technology. It adopts threefold ideas: 1) a deep autoencoder network that fully extracting latent features for precise reconstruction; 2) a proximal mapping operator to handle ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf>-norm regularization problem, leading to a stable and sparse representation of noise; and 3) the momentum acceleration scheme coupled with mini-batch gradient descent algorithm to accelerate training convergence. Experimental results on real images demonstrate that our proposed autoencoder network significantly outperforms existing approaches.