【導(dǎo)讀】第1周——第2周調(diào)研、收集材料，完成開題報(bào)告；第3周第4周分析、確定方案；第5周第11周方案詳細(xì)設(shè)計(jì)；第16周完善論文，答辯。據(jù)量最大，處理最難且研究最新的信息，應(yīng)用前途十分看好。解密技術(shù)和基于混沌的圖像加解密技術(shù)被提出。20世紀(jì)60年代，人們發(fā)現(xiàn)了一種特殊的自然現(xiàn)象混沌。用系統(tǒng)產(chǎn)生的混沌序列對待加密的圖像信息近行加密。因?yàn)榛煦缦到y(tǒng)是一種復(fù)雜的非線性動。所以，許多基于混沌的圖像加密技術(shù)還在不斷出現(xiàn)，這對圖像加密技術(shù)的研究產(chǎn)。第二種技術(shù)在近些年發(fā)展很迅速，主要特點(diǎn)是混沌系統(tǒng)的初始值和參數(shù)有較好的敏感。所以混沌系統(tǒng)具有天生的密碼學(xué)特性?；煦缧蛄?，將得到的混沌序列再與圖像序列進(jìn)行異或運(yùn)算，從而實(shí)現(xiàn)了對圖像的加密。全性高，加密解密速度快，算法容易實(shí)現(xiàn)等優(yōu)點(diǎn)。

　　

【正文】 設(shè)計(jì)與實(shí)現(xiàn) [D]. 山東 濟(jì)南 .2021 [3] ,Chidamber :JPEG image encryption using fuzzy PN sequences[J].Signal,Image,and Video Processing,2021 [4] 黃鑫 .圖像加密技術(shù)及其進(jìn)展 [J].科技信息 ,2021(6):1922 [5] 陳銘 ,平西建 .基于 Arnold變換的圖像信息偽裝算法 [J].計(jì)算機(jī)應(yīng)用研究 ,2021(1):235237 [6] 鄒建成 ,鐵小勻 .數(shù)字圖像的二維 Arnold變換及其周期 [J]．北方工業(yè)大學(xué)學(xué)報(bào)， 2021， 12(1)：P10~14 [7] 馬進(jìn) ,盧雷 ,朱寧 .基于劃分思想的 Arnold變換算法 [A].中國電子學(xué)會 .第十六屆信息論學(xué)術(shù)年會論文集 [C].北京：電子工業(yè)出版社， 2021:9094 [8] 張勇 ,陳濱 .Logistic映射的有限字長研究 [J].電子科技大學(xué)學(xué)報(bào) ,2021,35(3):292316 [9] Guan ZH,Huang FJ,Guan image encryption algorithm[J],Phys Lett A 2021,346:153157 [10] 王亥 ,胡建棟 .改進(jìn)型 LogisticMap混沌擴(kuò)頻序列 [J].通信學(xué)報(bào) ,1997,18(8):7177 [11] 張連俊 .圖像混沌加密技術(shù)分析 [J].現(xiàn)代情報(bào) ,2021,25(8):118119 [12] 張雪鋒 ,羅軍 ,高川 .基于混沌序列的數(shù)字圖像加密算法 [J].計(jì)算機(jī)工程與應(yīng)用， 2021,4（ 19）： 6162 [13] Dang P P,Chau P enecryption for secure Inter multimedia application[J].IEEE Transactions on Consumer Electronics,2021,46(8):395403 [14] 孫鑫 ,易開祥 ,孫優(yōu)賢 .基于混沌系統(tǒng)的圖像加密算法 [J].計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào) ,2021,14(2):136139 石家莊鐵道大學(xué)四方學(xué)院畢業(yè)論文 19 致 謝 這次畢業(yè)論文能夠得以順利完成，包含了很多人的關(guān)懷和心血。在此，我要向他們表示深深的謝意！ 首先，在論文的選題構(gòu)思、課題研究及論文寫作過程中，每當(dāng)我遇到疑難問題時，鄭 老師 總是耐心地幫我分析，幫我度過了一個個難關(guān)。最終，我才能順利完成論文的創(chuàng)作。鄭 老師 嚴(yán)謹(jǐn)求實(shí)，認(rèn)真細(xì)致的工作作風(fēng)，深厚的知識積淀，使我受益匪淺。這些將在我今后的工作中給我指引和激勵。在此，謹(jǐn)向鄭 老師 致以深深的謝意。 其次，要感謝所有曾經(jīng)教我們電子信息的任課老師，老師們教會我的不僅僅是專業(yè)知識，更多的是對待學(xué)習(xí)、對待生活的態(tài)度。 感謝我宿舍的舍友們在做論文的過程中給我的建議和幫助。 感謝我的父母、學(xué)校領(lǐng)導(dǎo)和我的親戚朋 友們，在四年的大學(xué)生涯中，給我莫大的精神鼓舞和物質(zhì)的支持，才能使我順利地完成學(xué)業(yè)。 最后，感謝在百忙中抽出時間閱讀本文的各位評委。 石家莊鐵道大學(xué)四方學(xué)院畢業(yè)論文 20 附 錄 附錄 A 外文資料 The research of digital image processing technique Introduction Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation。 and processing of image data for storage, transmission, and representation for autonomous machine perception. This chapter has several objectives: (1)to define the scope ofthe field that we call image processing。 (2)to give a historical perspective of the origins of this field。 (3)to give an idea of the state of the art in image processing by examining some of the principal area in which it is applied。 (4)to discuss briefly the principal approaches used in digital image processing。 (5)to give an overview of the ponents contained in a typic 叭generalpurpose image processing system。 and (6) to provide direction to the books and other literature where image processing work normally is reporter. What Is Digital Image Processing? An image may be defined as a twodimensional function,f(x,y)， where x and y are spatial (plane) coordinates, and the amplitude of fat any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x, y, and digital image. Tire field of digital image processing refers to processing digital images by means of a digital puter. Note that a digital image is posed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike human who are limited to the visual band of the electromagic (E11)spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images 石家莊鐵道大學(xué)四方學(xué)院畢業(yè)論文 21 generated by sources that human are not accustomed to associating with image. these include ultrasound, electron microscopy, and putergenerated images. Thus, digital image processing enpasses a wide and varied field of application. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and puter vision, start ,sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of puting tire average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as puter vision whose ultimate goal is to use puter toemulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. This field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and puter vision. There are no clearcut boundaries in the continuum from image processing at one end to puter vision at the other. However， one useful paradigm is to consider three types of puterized processes is this continuum: how， mid, and highever processes. lowlevel processes involve primitive operation such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A lowlevel process is characterized by the fact that both its input and output are processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for puter processing, and classification (recognition) of individual process is characterized by the fact that its inputs generally are images,but its output is attributes extracted from those images (e. g., edges contours, and the identity of individual object). Finally, higherlevel processing involves making senseof an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive function normally associated with vision. Based on the preceding ments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing enpasses 石家莊鐵道大學(xué)四方學(xué)院畢業(yè)論文 22 processes whose inputs and outputs are images and, in addition,enpasses processes that extract attributes from images,up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text. Preprocessing that images, extracting (segmenting) the individual characters, describing the characters in a form suitable for puter processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even puter vision,depending on the level of plexity implied by the statement making cense. As will bee evident shortly, digital image processing, as we have defined it, is used successfully in a broad rang of areas of exceptional social and economic value. The concepts developed in the following chapters are the foundation for the methods used in those application areas. The Origins of Digital Image Processing One of the first applications of digital images was in the newspaper industry, when pictures~first sent by submarine cable between London and of the Bartlane cable picture transmission system in the early 1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three printing equipment coded pictures for cable transmission and then