本文選題：CMOS傳感器 + 列固定模式噪聲��； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：以小體積,功耗低,抗輻射等優(yōu)良特性著稱的CMOS傳感器為高分辨率太陽圖像的采集提供了可靠的支持。而高分辨率圖像的獲得為太陽大氣,太陽物理等科學(xué)研究提供了有力保障。但是,由于CMOS傳感器處理電路和模數(shù)轉(zhuǎn)換器之間的失配,傳感器之間的差異等會導(dǎo)致圖像數(shù)據(jù)中出現(xiàn)列固定模式噪聲(CFPN)。這些噪聲的存在不但降低了圖像的質(zhì)量,掩蓋了圖像細節(jié),而且對后期圍繞這些圖像展開的科學(xué)研究也產(chǎn)生了影響。因此,列固定模式噪聲的去除是非常有必要的。近年來,國內(nèi)外眾多學(xué)者根據(jù)列固定模式噪聲的分布特性和統(tǒng)計規(guī)律,提出了基于統(tǒng)計學(xué),變分,傅里葉結(jié)合濾波以及多尺度去噪法等多種算法。但是由于這些算法自身的局限性使去噪后的圖像出現(xiàn)模糊失真等現(xiàn)象。為了解決上述問題,本文在小波變換的基礎(chǔ)上提出了一種基于小波變換和雙濾波的去噪算法。本文首先以仿真實驗的方式展示算法執(zhí)行過程。算法的主要執(zhí)行過程為:首先,根據(jù)噪聲的產(chǎn)生機理以及存在形式,將原始圖像對數(shù)化并進行小波變換。其次,對小波域中的垂直分量進行中值濾波,去除其中的噪聲小波系數(shù)。接著,利用小波逆變換得到無噪圖像,并與對數(shù)化圖像做差提取初始噪聲。然后,對初始噪聲進行低通高斯濾波并指數(shù)化得到結(jié)果噪聲。最后,用原始圖像除以結(jié)果噪聲便得到消噪后的圖像。同時,本文選取了幾個目前已存在的固定模式噪聲消除方法與本文算法作對比,驗證所提算法的準確性和有效性。仿真結(jié)果表明,所提算法能夠消除94%的噪聲。對均值、峰值信噪比(PSNR)、結(jié)構(gòu)相似度(SSIM)和功率譜密度等評價指標的分析表明,本文算法可以得到更佳的消噪結(jié)果。為了檢驗算法對閾值參數(shù)的響應(yīng)程度,針對仿真實驗分別對比和分析了不同閾值情況下的消噪結(jié)果差異。分析結(jié)果表明所提算法對中值濾波窗口寬度以及高斯核的選取有較強的反映。利用本文算法對中國云南撫仙湖天文臺TiO波段以及美國大熊湖天文臺Ha波段2組數(shù)據(jù)進行消噪處理,所得結(jié)果表明,本文算法不但能夠準確去除噪聲,而且能夠最大限度的保留圖像細節(jié)信息,消噪后的圖像特征更加明顯。
[Abstract]:The CMOS sensor, known for its small size, low power consumption and radiation resistance, provides reliable support for the acquisition of high resolution solar images. The acquisition of high resolution images provides a powerful guarantee for the scientific research of the solar atmosphere and solar physics. However, because of the mismatch between the CMOS sensor processing circuit and the analog to digital converter, The difference between sensors will lead to the appearance of column fixed pattern noise (CFPN) in the image data. The existence of these noises not only reduces the quality of the image, covers the details of the image, but also has an impact on the later scientific research on the expansion of these images. Therefore, the removal of the column fixed pattern noise is very necessary. In order to solve the above problems, many scholars at home and abroad have proposed a variety of algorithms based on statistics, variation, Fourier combined filtering and multiscale denoising based on the distribution and statistical rules of the fixed pattern noise. But because of the limitations of these algorithms, the images after the denoising are blurred. On the basis of wavelet transform, a denoising algorithm based on wavelet transform and double filter is proposed. Firstly, the execution process of the algorithm is displayed in the simulation experiment. The main execution process of the algorithm is: firstly, the original image is logarithmic and wavelet transform is carried out according to the mechanism of noise generation and the existence form. Secondly, the wavelet domain is applied to the wavelet domain. The vertical component is filtered by the median filter to remove the noise wavelet coefficients. Then, the noise free image is obtained by using the wavelet inverse transform, and the initial noise is extracted from the logarithmic image. Then, the initial noise is reduced by the low pass Gauss filter and exponentially obtains the result noise. Finally, the noise is eliminated with the original image and the noise is de-noised. At the same time, a few existing fixed mode noise elimination methods are selected and compared with the algorithm in this paper to verify the accuracy and effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm can eliminate 94% noise. Value, peak signal to noise ratio (PSNR), structural similarity (SSIM) and power spectral density are evaluated. The analysis shows that the algorithm can get better noise elimination results. In order to test the response of the algorithm to the threshold parameters, the difference of noise elimination results under different threshold conditions is compared and analyzed. The results show that the proposed algorithm has a strong reflection on the width of the median filter window and the selection of the Gauss kernel. The algorithm is used to denoise the data of 2 groups in the TiO band of the Yunnan Fuxian Lake Observatory in China and the Ha band of the Big Bear Lake Observatory in the United States. The results show that the algorithm can not only remove the noise accurately, but also retain the details of the image to the maximum, and the image features after the noise elimination are more obvious.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP212

【參考文獻】

相關(guān)期刊論文 前10條

1 劉亞梅;;基于自適應(yīng)單向變分的高光譜圖像去條帶方法[J];激光與光電子學(xué)進展;2016年09期

2 張霞;孫偉超;帥通;孫艷麗;;基于小波變換的圖像條帶噪聲去除方法[J];遙感技術(shù)與應(yīng)用;2015年06期

3 胡寶鵬;周則明;孟勇;張水平;;矩匹配和變分方法相結(jié)合的MODIS條帶去除模型[J];系統(tǒng)工程與電子技術(shù);2016年03期

4 龔紹琦;張茜茹;王少峰;孫德勇;魯奕岑;國文哲;;面向水體的HY-1B/COCTS圖像條帶噪聲去除[J];國土資源遙感;2015年04期

5 周達標;李剛;王德江;賈平;;基于全變分的航空圖像條帶噪聲消除方法[J];光學(xué)學(xué)報;2014年11期

6 Zhong Liu;Jun Xu;Bo-Zhong Gu;Sen Wang;Jian-Qi You;Long-Xiang Shen;Ru-Wei Lu;Zhen-Yu Jin;Lin-Fei Chen;Ke Lou;Zhi Li;Guang-Qian Liu;Zhi Xu;Chang-Hui Rao;Qi-Qian Hu;Ru-Feng Li;Hao-Wen Fu;Feng Wang;Men-Xian Bao;Ming-Chan Wu;Bo-Rong Zhang;;New vacuum solar telescope and observations with high resolution[J];Research in Astronomy and Astrophysics;2014年06期

7 王欣昕;楊豐;田野;;CMOS圖像傳感器技術(shù)在醫(yī)療領(lǐng)域的應(yīng)用[J];自動化與信息工程;2014年03期

8 王春陽;郭增長;王雙亭;蘆碧波;;雙邊濾波與矩匹配融合的高光譜影像條帶噪聲去除方法[J];測繪科學(xué)技術(shù)學(xué)報;2014年02期

9 張聚;王陳;程蕓;;小波與雙邊濾波的醫(yī)學(xué)超聲圖像去噪[J];中國圖象圖形學(xué)報;2014年01期

10 馬寧;馬英;周則明;羅立民;;基于變分的MODIS數(shù)據(jù)條帶噪聲去除方法[J];解放軍理工大學(xué)學(xué)報(自然科學(xué)版);2013年05期

，

本文編號：2047896