摘要：传统SAR雷达是一种相干系统，被广泛用于军事、农业、测绘等领域，近年来发展迅速，其图像具有斑点噪声这一特点。传统图像增强算法针对SAR复图像主要采用滤波算法，而使用传统串行处理，计算时间长，无法满足实时处理的需求。GPU的并行运算具有更多线程数量，对重复数据的计算能力较强，可以改造应用于SAR复图像处理。

本文主要针对SAR图像的特点，研究几种常见增强算法，包括双边滤波和BM3D两种较为常见的增强算法。基于NVIDIA的CUDA平台对常见的SAR图像增强算法进行可行性分析，对传统双边滤波算法进行并行化改进，针对几组不同大小的图像，分别计算双边滤波增强处理在串行和并行计算下的时间花费，计算其加速比。

仿真结果表明，使用GPU进行并行计算处理后，图像的质量与传统CPU处理基本保持一致，而在处理速度上较传统方法提升明显。

关键词  SAR复图像  双边滤波  CUDA  

毕业设计说明书外文摘要

Title  Research of Complex Images Enhancement Simulation Based on GPU  

Abstract：Traditional SAR radar is a kind of coherent system, which is widely used in the fields of military, agriculture and surveying and mapping. It has developed rapidly in recent years, and its image has the characteristic of speckle noise. The traditional image enhancement algorithm mainly uses the filtering algorithm for the SAR complex image, and uses the traditional serial processing, the computation time is long, can not satisfy the real-time processing demand. GPU parallel computing has more threads and the ability to calculate the data is strong It can be modified for SAR complex image processing.

The paper focus on the characteristics of SAR image, and study several common enhancement algorithms, including bilateral filtering and BM3D. Based on NVIDIA's CUDA platform, the feasibility of the SAR image enhancement algorithm is analyzed, and the traditional bilateral filtering algorithm is improved in parallel. For different image sizes, the time of the two-sided filter denoising processing in the serial and parallel algorithms is calculated separately  then  alculate its acceleration ratio.

The simulation can tell that the quality of the image is consistent with the traditional CPU processing, and the processing has much faster speed than the traditional method obviously.

Keywords  SAR Complex Images   Bilateral Filtering   CUDA  

目录

1  引言 1

1.1  SAR合成孔径雷达研究背景 1

1.2  SAR图像处理现状 2

1.3  本文内容及安排 3

2  GPU及CUDA架构的发展 5

2.1  CPU、GPU结构分析与串并行研究 5

2.1.1  GPU发展简介 5

2.1.2  GPU发展简介 6

2.1.3  GPU与CPU架构对比 8

2.2  CUDA发展及架构分析 8

2.3  GPU并行计算性能分析 10

2.3.1  并行计算介绍 10

2.3.2  仿真平台介绍 10

2.3.3  GPU上的矩阵乘法 11

2.3.4  GPU上的快速傅里叶变换（FFT）和逆变换（IFFT） 12

3  典型图像增强算法及GPU平台可行性分析 16

3.1  图像质量判断及PSNR 16

3.2  直方图均衡化 16

3.2.1  直方图均衡化原理 16

3.2.2  直方图均衡化可行性分析 16

3.3  中值滤波 17

3.3.1  中值滤波原理 17

3.3.2  中值滤波可行性分析 17

3.4  BM3D算法 17

3.4.1  BM3D增强算法原理 17

3.4.2  BM3D增强算法可行性分析 19