Deep Generative Adversarial Networks for the Sparse Signal Denoising

Abstract

In many practical denoising problems, noisy signal contains lots of sparse information, which is helpful for denoising. However, common denoising methods such as low-pass filtering methods and Wavelet denoising methods have a number of limitations like the rigid projection space or loss of high frequency component. In this paper, we propose a deep learning framework based on Generative Adversarial Networks (GANs) to deal with the sparse denoising tasks. We design the Generative Network (G-net) as denoising model with three parts, which are encoding part, denoising part and linear recovery part. To maintain the original features of the data, we utilize the Discriminator Network (D-net) to help the denoising model G-net learn. The experimental results show that our framework is more effective than some traditional methods and state-of-art deep learning methods. In particular, sparse denoising GANs can recover details of picture better in the MNIST image tasks.