Research on Digital Signal Denoising Techniques Based on Convolutional Neural Networks

In recent years, convolutional neural networks (CNNs) have demonstrated considerable potential in the domain of digital signal denoising, effectively overcoming the constraints of conventional filtering and spectral methodologies. Digital signals are frequently subjected to a variety of noise interferences, which not only impair the signal quality but also introduce errors in the data. In this work, we propose a novel multi-level convolutional neural network (CNN) denoising architecture that combines residual learning and an attention mechanism to selectively amplify key features while removing noise. The model structure incorporates a dual transformation of the time domain and the frequency domain, enabling the capture of complex dependencies in diverse noise patterns through the use of dilated convolution and adaptive jump connection. Furthermore, a multi-scale feature fusion strategy has been devised with the objective of enhancing the denoising performance of the model at varying noise levels, thereby improving its robustness to different signal-to-noise ratios. The experimental results demonstrate that the proposed method exhibits a notable superiority over the current state-of-the-art denoising techniques in terms of signal fidelity and detail preservation. Furthermore, it is evident that the method is adaptable to a diverse range of noise scenarios.