Two-Stream Multiplicative Heavy-Tail Noise Despeckling Network With Truncation Loss

In recent years, deep learning algorithms for speckle noise removal have attracted much attention. However, speckle noise is strongly heavy-tailed and signal-dependent, which makes it difficult to remove. In this article, we propose a two-stream convolutional neural network (CNN) with hybrid truncation loss to eliminate multiplicative noise (HTNet). HTNet combines the major task of multiplicative noise removal and the auxiliary task of noise estimation to improve the despeckling effect while preserving texture details. The main branch of HTNet is composed of a feature extraction block (FEB) and an improved U-Net that can extract multiscale information, which is mainly used for speckle noise removal. The noise estimation auxiliary branch is designed to fit the speckle noise. A hybrid truncation loss function is applied for robust estimation of heavy-tailed distribution instead of mean squared error (MSE). Extensive experimental results show that HTNet can effectively remove speckle noise and outperforms the state-of-the-art methods on both simulated and real-SAR images. In addition, HTNet has advantages over textured images.