Convolutional Neural Network and Guided Filtering for SAR Image Denoising

Shuaiqi Liu,Lele Gao,Jie Zhao,Tong Liu,Qi Hu,Chong Wang,Hailiang Li

doi:10.3390/rs11060702

Abstract

Coherent noise often interferes with synthetic aperture radar (SAR), which has a huge impact on subsequent processing and analysis. This paper puts forward a novel algorithm involving the convolutional neural network (CNN) and guided filtering for SAR image denoising, which combines the advantages of model-based optimization and discriminant learning and considers how to obtain the best image information and improve the resolution of the images. The advantages of proposed method are that, firstly, an SAR image is filtered via five different level denoisers to obtain five denoised images, in which the efficient and effective CNN denoiser prior is employed. Later, a guided filtering-based fusion algorithm is used to integrate the five denoised images into a final denoised image. The experimental results indicate that the algorithm cannot eliminate noise, but it does improve the visual effect of the image significantly, allowing it to outperform some recent denoising methods in this field.

Highlights

Synthetic aperture radar (SAR) is a significant coherent imaging system that generates high-resolution images of terrain and targets
The training sets and training process of the convolutional neural network (CNN) denoisers used in this paper are the same as those described in the literature [12]
The denoiser model training platform used was Matlab R2014b which is from Mathworks company in Natick, MA, USA, the CNN toolbox was MatConvnet (MatConvnet-1.0-beta24, Mathworks, Natick, Massachusetts, USA), and the GPU platform was Nvidia Titan X Quadro K6000 (Santa Clara, California, USA) which is from NVIDIA Corporation in Santa Clara, CA, USA

Summary

Introduction

Synthetic aperture radar (SAR) is a significant coherent imaging system that generates high-resolution images of terrain and targets. Since the spatial filtering tends to darken the denoised SAR images, denoising algorithms based on the transform domain have been developed and have had remarkable achievements in recent years. The general procedure of transform domain filtering is, firstly, to transform the original images; the noise-free coefficients are estimated; and, the denoised images are achieved via the inverse-transform from the processed coefficients. The statistical relationship between a pixel and its neighboring pixels is used mostly in the speckle suppression algorithms, and they do not utilize the information of similar local regions or the natural statistical characteristics of the whole image, which could be utilized to enhance the image denoising effect further

Methods

Results

Conclusion