本文選題：圖像壓縮 + 圖像去噪��； 參考：《長(zhǎng)春理工大學(xué)》2017年碩士論文

【摘要】：近年來,我國(guó)發(fā)射了大量衛(wèi)星,從北斗導(dǎo)航系列衛(wèi)星到各種氣象、資源勘探衛(wèi)星,可以說,我國(guó)的航天遙感技術(shù)取得了長(zhǎng)足發(fā)展。伴隨著這么多的衛(wèi)星發(fā)射升空,每天都有海量的數(shù)據(jù)從各種傳感器上接收下來。與此同時(shí),在采集和回傳階段,數(shù)據(jù)或多或少都會(huì)受到許多不確定因素的影響而被污染。因此,在傳輸前對(duì)其進(jìn)行壓縮,以及在接收后進(jìn)行噪聲去除已成為廣泛關(guān)注的研究熱點(diǎn)。小波分析的時(shí)頻局部特性和多分辨率分析特性,可以把圖像數(shù)據(jù)中的重要信息有效地提取出來,因此,它在圖像處理領(lǐng)域得到了非常廣泛的應(yīng)用。文章的主要工作是把接收到的圖像數(shù)據(jù)經(jīng)小波變換后如何更好地對(duì)其進(jìn)行噪聲剔除,以及圖像傳輸前的數(shù)據(jù)壓縮。小波變換去噪的方法已有很多種,本文著重考慮閾值去噪方法,討論了去噪過程中閾值和閾值函數(shù)的選取,并對(duì)傳統(tǒng)方法進(jìn)行了改進(jìn),最后通過仿真,檢驗(yàn)了改進(jìn)方法的可行性及去噪效果。分析了經(jīng)典小波編碼算法,針對(duì)SPIHT編碼算法需要大的存儲(chǔ)空間和很多不必要運(yùn)算的缺點(diǎn),提出了一種改進(jìn)的SPIHT編碼算法,彌補(bǔ)了之前編碼過程中需要大存儲(chǔ)空間并耗時(shí)的不足。
[Abstract]:In recent years, China has launched a large number of satellites, from Beidou navigation series satellites to various meteorological and resource exploration satellites. It can be said that China's space remote sensing technology has made great progress. With so many satellite launches, huge amounts of data are received from various sensors every day. At the same time, at the stage of collection and return, the data are more or less polluted by many uncertain factors. Therefore, compression before transmission and noise removal after receiving have become the focus of attention. The time-frequency and multi-resolution characteristics of wavelet analysis can effectively extract important information from image data, so it has been widely used in the field of image processing. The main work of this paper is how to eliminate the noise of the received image data after wavelet transform, and how to compress the data before the image transmission. There are many kinds of denoising methods based on wavelet transform. This paper focuses on the threshold denoising method, discusses the selection of threshold and threshold function in the process of de-noising, and improves the traditional method. The feasibility of the improved method and the effect of denoising are tested. This paper analyzes the classical wavelet coding algorithm, aiming at the disadvantages of SPIHT coding algorithm which needs large storage space and many unnecessary operations, an improved SPIHT coding algorithm is proposed, which makes up for the shortage of large storage space and time consuming in the previous coding process.
【學(xué)位授予單位】：長(zhǎng)春理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張倩;;基于雙重離散小波變換的遙感圖像去噪算法[J];國(guó)土資源遙感;2015年04期

2 李金倫;崔少輝;汪明;;基于改進(jìn)中值濾波和提升小波變換的閾值去噪方法研究[J];應(yīng)用光學(xué);2014年05期

3 吳秀君;;改進(jìn)提升小波閾值算法在信號(hào)去噪中的應(yīng)用[J];激光雜志;2014年08期

4 單銳;齊越;劉文;;一種改進(jìn)的小波閾值去噪算法[J];蘭州理工大學(xué)學(xué)報(bào);2014年04期

5 周潞;祝忠明;譚帥;;小波收縮閾值消噪在海洋電磁中的應(yīng)用[J];信息通信;2013年10期

6 高文仲;陳志云;曾秋梅;;小波閾值圖像去噪算法改進(jìn)[J];華東師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年06期

7 孫永生;劉大健;秦蒙;;多幅圖像中值法在濾除噪聲中的應(yīng)用[J];電視技術(shù);2012年23期

8 常亮亮;王廣龍;;基于中值濾波和提升小波分析的圖像去噪方法研究[J];應(yīng)用光學(xué);2012年05期

9 王小兵;孫久運(yùn);湯海燕;;基于小波變換的圖像混合噪聲自適應(yīng)濾波算法[J];微電子學(xué)與計(jì)算機(jī);2012年06期

10 許光宇;檀結(jié)慶;鐘金琴;;自適應(yīng)的有效非局部圖像濾波[J];中國(guó)圖象圖形學(xué)報(bào);2012年04期

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