【正文】 the research of image processing, the information entropy has attracted more and more attention. In order to find the fast and effective image processing method, information theory is used more and more frequently in the image processing technology. In this paper, through the further discussion on concept of entropy, analyzes its application in image processing, such as image segmentation, image registration, face recognition, feature detection etc.This paper introduces the application of information entropy in image processing, summarizes some basic concepts based on the definition of entropy, mutual information. And the information entropy of image processing especially for image segmentation and image registration. Finally realize the information entropy in image registration.Keywords： Information entropy, Mutual information, Image segmentation,Image registration河北工程大學(xué)畢業(yè)設(shè)計(jì)（論文） 3 目 錄摘 要......................................................................................................................................1ABSTRACT...............................................................................................................................2目 錄..................................................................................................................................31 引言........................................................................................................................................5 信息熵的概念 .................................................................................................................5 信息熵的基本性質(zhì)及證明 .............................................................................................6 單峰性.......................................................................................................................6 對(duì)稱性.......................................................................................................................7 漸化性.......................................................................................................................7 展開(kāi)性.......................................................................................................................7 確定性.......................................................................................................................82 基于熵的互信息理論 ............................................................................................................9 互信息的概述.................................................................................................................9 互信息的定義.................................................................................................................9 熵與互信息的關(guān)系.........................................................................................................93 信息熵在圖像分割中的應(yīng)用..............................................................................................11 圖像分割的基本概念 ..................................................................................................11 圖像分割的研究現(xiàn)狀 .............................................................................................11 圖像分割的方法.....................................................................................................11 基于改進(jìn)粒子群優(yōu)化的模糊熵煤塵圖像分割 ............................................................12 基本粒子群算法.....................................................................................................12 改進(jìn)粒子群優(yōu)化算法.............................................................................................13 Morlet 變異 ..............................................................................................................13 改建粒子群優(yōu)化的圖像分割方法..........................................................................14 實(shí)驗(yàn)結(jié)果及分析.....................................................................................................16 一種新信息熵的定義及其在圖像分割中的應(yīng)用 .......................................................19 香農(nóng)熵的概念及性質(zhì).............................................................................................19 一種信息熵的定義及證明.....................................................................................19 信息熵計(jì)算復(fù)雜性分析.........................................................................................21 二維信息熵閾值法.................................................................................................22 二維信息熵閾值法的復(fù)雜性分析.........................................................................24 結(jié)論及分析............................................................................................................254 信息熵在圖像配準(zhǔn)中的應(yīng)用..............................................................................................27 圖像配準(zhǔn)的基本概述 ...................................................................................................27 基于互信息的圖像配準(zhǔn) ...............................................................................................27 POWELL 算法 ..................................................................................................................28河北工程大學(xué)畢業(yè)設(shè)計(jì)（論文） 4 變換 ...............................................................................................................................28 平移變換 .................................................................................................................29 旋轉(zhuǎn)變換 .................................................................................................................30 基于互信息的圖像配準(zhǔn)的設(shè)計(jì)與實(shí)現(xiàn) .......................................................................31 總體設(shè)計(jì)思路和圖像配準(zhǔn)實(shí)現(xiàn) ............................................................................31 直方圖 ......................................................................................................................33 聯(lián)合直方圖 .............................................................................................................33 灰度級(jí)差值技術(shù) ............................