微光ICCD数字图像去噪的研究
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摘要
微光图像是多次光电转换和电子倍增之后的输出图像,与普通的可见光图像相比,具有较低的信噪比,带有明显的随机闪烁噪声,不利于人眼观察和识别。因此,噪声是微光成像系统中的重要因素,它决定了整个系统的性能。可见,对其噪声特性进行深入研究,改善成像质量,提高信噪比,对推动和拓展它的应用领域,具有重大的现实意义。
本文以微光ICCD的图像噪声为研究对象,深入的分析和研究其噪声特点,并利用现有的数字图像处理方法进行去噪实验研究,以期改善图像质量。在噪声研究的过程中,既关注它的理论性和创新性、又注意其在实际操作中的可用性。因此,从噪声理论的发展和图像质量的改善,本文的研究都具有重要意义。
本论文开展的主要研究工作有如下几个方面：
(1)讨论了微光ICCD的结构和工作机理,分析了噪声产生的来源、因素和特点,从时间域随机过程和空间域随机过程统一的角度建立了微光ICCD成像系统的噪声模型。分析表明,对于像增强CCD成像系统,它的噪声主要来自像增强器部分和CCD本身。而对于噪声的最终表现形式来说,一般上可分为两类：一类为电子噪声,比如入射光子的涨落噪声、光电转换引起的量子涨落噪声,还有光电阴极暗发射产生的噪声、MCP的探测效率以及它的增益涨落噪声等。这一类的噪声是由光子和电子随时空变化的固有性质所决定的,它是一种起伏性的、随机的涨落噪声。特征是在荧光屏上呈现为微弱性的、细小的颗粒状闪烁亮点。从根本上来说,这种噪声是不能被消除的,只有通过图像处理技术加以抑制。另一类为离子噪声,它是由器件内部游离的离子所引起的,比如说器件内部空间或者是吸附在电极上的、具有吸附或解析的动态过程。从荧屏上的表现来看也是随时空变化的闪烁亮点,但比来自电子噪声亮点要明亮一些,体积也要大一些。不过,这种离子噪声不是固有的,可以通过光电阴极的改善、提高MCP以及器件电极的制造工艺以及保证真空度来达到降低的目的。
(2)在研究均值滤波方法的基础上,对参考阈值去噪方法进行了实验研究。它先求取滤波窗口中心像素点之外的像素点的灰度均值,然后与中心像素点的灰度值作差并取它们的绝对值,最后同阈值进行比较,根据比较结果决定最终的像素点灰度值。
(3)实验研究了用多次一维中值滤波来代替二维中值滤波的一种快速算法,并采用改进性的极值中值自适应去噪方法。它首先是根据判定标准将像素点分为噪声和信号两大类,根据空间相关性原则将噪声点由其邻域的中值来代替,同时根据一些条件变化,自动地改变滤波窗口的大小,因此更好地保护了图像细节。
(4)众值滤波技术的主导思想是用数学统计上出现几率最大的灰度值作为处理结果,帧积分方法是利用图像噪声空域随机不相关的特性,将不同的时刻的两幅或两幅图像以上的对应像素点的灰度值相加。本文将此方法与帧积分进行结合实验研究,取得了良好的效果。
(5)本文采用基于差分图像的多尺寸广义形态-差值滤波器。即在多尺寸形态滤波的基础上,经由差分算法处理后得到一组不同分辨率的图像,再将每一幅图像应用差值滤波去噪后,与最后一级的图像结果相加。这样所得的目标图像,既保留了图像细节又很好的去除了噪声。
(6)探讨了小波分析在图像去噪上的应用,采用了将“硬阈值化”和“软阈值化”结合起来构造的一类新的阈值函数来处理微光图像的去噪问题。即首先对预处理后的图像进行一层Haar小波分解,根据新的结合软硬阈值化的阈值函数对水平、对角和垂直方向的高频小波系数进行阈值处理,最后根据处理后的小波系数进行重构,就得到了图像的去噪结果。
本论文的创新点有：
(1)提出了众值帧积分方法。实验结果表明,众值帧积分能有效地抑制微光图像中的亮点噪声和暗点噪声,对图像边缘的保护特性比帧积分好,具有重要实用价值。峰值信噪比结果为：帧积分处理的PSNR仅为29.32,而众值帧积分处理的PSNR可达35.57。
(2)首次采用了一种改进性的广义形态-差值微光图像去噪方法,它既去除了噪声又保留了细节的目标图像。峰值信噪比表明,广义形态-差值滤波处理过的图像要高于多帧平均滤波处理过的图像。多帧平均滤波的PSNR为34.57,而广义形态-差值滤波的PSNR为41.52。
(3)首次使用了将“硬阈值化”和“软阈值化”结合起来构造的一类新的阈值函数应用于微光图像去噪处理。结合软硬阈值化方法而形成的阈值函数有良好的连续性且方便调节,最终图像的信噪比有了进一步提高,具有较高的应用价值。峰值信噪比结果分别为：帧积分的PSNR为37.49,硬阈值法的PSNR为44.76,软阈值法的PSNR为44.81,本文算法的PSNR为45.87。
The low light level images are different from general visible light images. It is formed by the process of photoelectric conversion and electron multiplier. Due to the low illuminance and poor background, there are obvious random flicker noises superimposed on the output images. The lower the illuminance is, the more serious the noises are. At the same time the brightness and contrast also decreased. Thus the obtained information is of very low signal to noise ratio. So the output images are without enough resolution and contrast to be observed and identified. Obviously, the noise is the key factor that influences the performance of the low light level imaging system. Therefore it is of great practical significance to investigate the noise characteristics to improve the image quality, enhance the signal to noise ratio, promote and expand the application fields.
In this paper, based on the image noise of the low light level ICCD, the characteristic of the noise was studied. The experimental research on denoising using the existing ditital image processing method were also be done in order to improve the quality of the image. During the research, much attention was paid not only to the theory and innovation, but also to the actual operation availability. All of these work has great important theoretical and practical significance on the development of noise theory and image quality of the low light level imaging system.
The main research is as follows:
(1)The structure, working mechanism, together with the sources and characteristics of the noise were discussed. And model of the noise was established from a unified perspective between the time domain stochastic process and the space domain of stochastic process. It was indicated that the noise sources come from imaging system and CCD itself. The noise can be divided into two kinds as far as the final forms of the noise concerned. One kins is the electronic noise, such as the incident photon noise, the quantum fluctuation noise in the photoelectric conversion process, the dark noise from the photoelectric cathode, the noise from MCP detection efficient and gain fluctuation. This kind of noise is randomly fluctuate and determined by the inherent statistical nature of the changes of the photons and electrons with time and space.This noise shows on the screen as small and faint granular twinkles and cannot be eliminated. Only though the image processing method, this kind of noise can be inhibited. Another kind of noise is the ion noise and come from the free ions in the device, such as the absorbed ions in the internal of the device and the electrode, these ions has a dynamic process between the adsorption and desorption. This kind of noise shows on the screen as a large, bright random twinkles. This noise is not inherent an can be eliminated by improving the quality of photoelectric cathode, MCP, electrode formation process and the vacuum.
(2) Based on the mean filtering method, the reference denoising method was used to improve the image quality of the low light level. In this method, firstly, the absolute value of the difference between the pixel gray of the filter window and the mean value of all pixels gray, then compared with the threshold value and the final pixel gray value can be ultimately determined.
(3) In the experiment, a kind of faster algorithm by using the multiple one-dimensional median filter to replace the2D median filtering was introduced, and a improved extremum median adaptive denosing method was adopted to improve the image quality in this paper. In this method, firstly, the pixels was divided into noise and signal according to the judging standard, then the noise point was replace by the mean value of neighborhood according to the spatial correlation principle. At the same time, the filter window size can be automatically changed according to the change of conditions. So the detail of the image can be well protected.
(4) The value filtering technology is the dominant ideology in mathematics and statistics on the occurrence probability of the maximum gray value can be used as the result. By using spatial random of the noise,the frame integral method is to sum up the corresponding pixel gray value from two images with different time. In this paper, this method and the method of frame integral were combined in the experimental study and achieved good results.
(5) Based on the difference image of multi dimensional generalized morphological-difference filter. E.g. based on multi dimensional morphological filter, a group difference image containing different sizes of noise and image information was obtained. By denoising using difference and sum up with the final morphological fileter, the targer image not only trtain the image details but also remove much noise.
(6) The application of the wavelet analysis on image denoising was studied. By forming a new class of threshold function combining the " hard threshold " with " soft thresholding ", the image noise can be removed. Firstly, decomposing the image using a layer of Haar wavelet, then processing the new combination of soft and hard threshold threshold function on horizontal, diagonal and vertical direction of the high frequency wavelet coefficient threshold. Finally reconstructing the wavelet coefficient, the image denoising results can be shown..
The innovation in this paper is as follows:
(1)On the basis of the value idea and frame integral method, the frame integral method is put forward. It is indicated that this method can be inhibit the bright noise and dark noise of the low light level image. At the same time, the protection performance on the image edge is better than the frame integral method and of great application value. The PSNR is29.32and35.57for these two methods, respectively.
(2) According to the principle of generalized morphological,differential filter and difference image, an improved generalized morphological-difference image denoising method was proposed. It not only eliminates the noise but also keep details of the target image. The generalized morphological-differential filtering for the processed image is higher than average frames of image filter processing. The PSNR of the laster is34.57, while the former is41.52.
(3) In order to the shortcoming of the soft and hard thresholding methods, a new thresholding function was constructed by combining the sofe and had thresholding methods and used to denoising on the low light level image. This method has good continuity and convenient adjustment, the final image SNR has been further improved, and has higher application value. The PSNR is37.49,44.76and44.81for the frame integral method, hard threshold method and soft threshold methed, respectively. While the PSNR is45.87for the algorithm adopted in this paper.
本文以微光ICCD的图像噪声为研究对象,深入的分析和研究其噪声特点,并利用现有的数字图像处理方法进行去噪实验研究,以期改善图像质量。在噪声研究的过程中,既关注它的理论性和创新性、又注意其在实际操作中的可用性。因此,从噪声理论的发展和图像质量的改善,本文的研究都具有重要意义。
本论文开展的主要研究工作有如下几个方面：
(1)讨论了微光ICCD的结构和工作机理,分析了噪声产生的来源、因素和特点,从时间域随机过程和空间域随机过程统一的角度建立了微光ICCD成像系统的噪声模型。分析表明,对于像增强CCD成像系统,它的噪声主要来自像增强器部分和CCD本身。而对于噪声的最终表现形式来说,一般上可分为两类：一类为电子噪声,比如入射光子的涨落噪声、光电转换引起的量子涨落噪声,还有光电阴极暗发射产生的噪声、MCP的探测效率以及它的增益涨落噪声等。这一类的噪声是由光子和电子随时空变化的固有性质所决定的,它是一种起伏性的、随机的涨落噪声。特征是在荧光屏上呈现为微弱性的、细小的颗粒状闪烁亮点。从根本上来说,这种噪声是不能被消除的,只有通过图像处理技术加以抑制。另一类为离子噪声,它是由器件内部游离的离子所引起的,比如说器件内部空间或者是吸附在电极上的、具有吸附或解析的动态过程。从荧屏上的表现来看也是随时空变化的闪烁亮点,但比来自电子噪声亮点要明亮一些,体积也要大一些。不过,这种离子噪声不是固有的,可以通过光电阴极的改善、提高MCP以及器件电极的制造工艺以及保证真空度来达到降低的目的。
(2)在研究均值滤波方法的基础上,对参考阈值去噪方法进行了实验研究。它先求取滤波窗口中心像素点之外的像素点的灰度均值,然后与中心像素点的灰度值作差并取它们的绝对值,最后同阈值进行比较,根据比较结果决定最终的像素点灰度值。
(3)实验研究了用多次一维中值滤波来代替二维中值滤波的一种快速算法,并采用改进性的极值中值自适应去噪方法。它首先是根据判定标准将像素点分为噪声和信号两大类,根据空间相关性原则将噪声点由其邻域的中值来代替,同时根据一些条件变化,自动地改变滤波窗口的大小,因此更好地保护了图像细节。
(4)众值滤波技术的主导思想是用数学统计上出现几率最大的灰度值作为处理结果,帧积分方法是利用图像噪声空域随机不相关的特性,将不同的时刻的两幅或两幅图像以上的对应像素点的灰度值相加。本文将此方法与帧积分进行结合实验研究,取得了良好的效果。
(5)本文采用基于差分图像的多尺寸广义形态-差值滤波器。即在多尺寸形态滤波的基础上,经由差分算法处理后得到一组不同分辨率的图像,再将每一幅图像应用差值滤波去噪后,与最后一级的图像结果相加。这样所得的目标图像,既保留了图像细节又很好的去除了噪声。
(6)探讨了小波分析在图像去噪上的应用,采用了将“硬阈值化”和“软阈值化”结合起来构造的一类新的阈值函数来处理微光图像的去噪问题。即首先对预处理后的图像进行一层Haar小波分解,根据新的结合软硬阈值化的阈值函数对水平、对角和垂直方向的高频小波系数进行阈值处理,最后根据处理后的小波系数进行重构,就得到了图像的去噪结果。
本论文的创新点有：
(1)提出了众值帧积分方法。实验结果表明,众值帧积分能有效地抑制微光图像中的亮点噪声和暗点噪声,对图像边缘的保护特性比帧积分好,具有重要实用价值。峰值信噪比结果为：帧积分处理的PSNR仅为29.32,而众值帧积分处理的PSNR可达35.57。
(2)首次采用了一种改进性的广义形态-差值微光图像去噪方法,它既去除了噪声又保留了细节的目标图像。峰值信噪比表明,广义形态-差值滤波处理过的图像要高于多帧平均滤波处理过的图像。多帧平均滤波的PSNR为34.57,而广义形态-差值滤波的PSNR为41.52。
(3)首次使用了将“硬阈值化”和“软阈值化”结合起来构造的一类新的阈值函数应用于微光图像去噪处理。结合软硬阈值化方法而形成的阈值函数有良好的连续性且方便调节,最终图像的信噪比有了进一步提高,具有较高的应用价值。峰值信噪比结果分别为：帧积分的PSNR为37.49,硬阈值法的PSNR为44.76,软阈值法的PSNR为44.81,本文算法的PSNR为45.87。
The low light level images are different from general visible light images. It is formed by the process of photoelectric conversion and electron multiplier. Due to the low illuminance and poor background, there are obvious random flicker noises superimposed on the output images. The lower the illuminance is, the more serious the noises are. At the same time the brightness and contrast also decreased. Thus the obtained information is of very low signal to noise ratio. So the output images are without enough resolution and contrast to be observed and identified. Obviously, the noise is the key factor that influences the performance of the low light level imaging system. Therefore it is of great practical significance to investigate the noise characteristics to improve the image quality, enhance the signal to noise ratio, promote and expand the application fields.
In this paper, based on the image noise of the low light level ICCD, the characteristic of the noise was studied. The experimental research on denoising using the existing ditital image processing method were also be done in order to improve the quality of the image. During the research, much attention was paid not only to the theory and innovation, but also to the actual operation availability. All of these work has great important theoretical and practical significance on the development of noise theory and image quality of the low light level imaging system.
The main research is as follows:
(1)The structure, working mechanism, together with the sources and characteristics of the noise were discussed. And model of the noise was established from a unified perspective between the time domain stochastic process and the space domain of stochastic process. It was indicated that the noise sources come from imaging system and CCD itself. The noise can be divided into two kinds as far as the final forms of the noise concerned. One kins is the electronic noise, such as the incident photon noise, the quantum fluctuation noise in the photoelectric conversion process, the dark noise from the photoelectric cathode, the noise from MCP detection efficient and gain fluctuation. This kind of noise is randomly fluctuate and determined by the inherent statistical nature of the changes of the photons and electrons with time and space.This noise shows on the screen as small and faint granular twinkles and cannot be eliminated. Only though the image processing method, this kind of noise can be inhibited. Another kind of noise is the ion noise and come from the free ions in the device, such as the absorbed ions in the internal of the device and the electrode, these ions has a dynamic process between the adsorption and desorption. This kind of noise shows on the screen as a large, bright random twinkles. This noise is not inherent an can be eliminated by improving the quality of photoelectric cathode, MCP, electrode formation process and the vacuum.
(2) Based on the mean filtering method, the reference denoising method was used to improve the image quality of the low light level. In this method, firstly, the absolute value of the difference between the pixel gray of the filter window and the mean value of all pixels gray, then compared with the threshold value and the final pixel gray value can be ultimately determined.
(3) In the experiment, a kind of faster algorithm by using the multiple one-dimensional median filter to replace the2D median filtering was introduced, and a improved extremum median adaptive denosing method was adopted to improve the image quality in this paper. In this method, firstly, the pixels was divided into noise and signal according to the judging standard, then the noise point was replace by the mean value of neighborhood according to the spatial correlation principle. At the same time, the filter window size can be automatically changed according to the change of conditions. So the detail of the image can be well protected.
(4) The value filtering technology is the dominant ideology in mathematics and statistics on the occurrence probability of the maximum gray value can be used as the result. By using spatial random of the noise,the frame integral method is to sum up the corresponding pixel gray value from two images with different time. In this paper, this method and the method of frame integral were combined in the experimental study and achieved good results.
(5) Based on the difference image of multi dimensional generalized morphological-difference filter. E.g. based on multi dimensional morphological filter, a group difference image containing different sizes of noise and image information was obtained. By denoising using difference and sum up with the final morphological fileter, the targer image not only trtain the image details but also remove much noise.
(6) The application of the wavelet analysis on image denoising was studied. By forming a new class of threshold function combining the " hard threshold " with " soft thresholding ", the image noise can be removed. Firstly, decomposing the image using a layer of Haar wavelet, then processing the new combination of soft and hard threshold threshold function on horizontal, diagonal and vertical direction of the high frequency wavelet coefficient threshold. Finally reconstructing the wavelet coefficient, the image denoising results can be shown..
The innovation in this paper is as follows:
(1)On the basis of the value idea and frame integral method, the frame integral method is put forward. It is indicated that this method can be inhibit the bright noise and dark noise of the low light level image. At the same time, the protection performance on the image edge is better than the frame integral method and of great application value. The PSNR is29.32and35.57for these two methods, respectively.
(2) According to the principle of generalized morphological,differential filter and difference image, an improved generalized morphological-difference image denoising method was proposed. It not only eliminates the noise but also keep details of the target image. The generalized morphological-differential filtering for the processed image is higher than average frames of image filter processing. The PSNR of the laster is34.57, while the former is41.52.
(3) In order to the shortcoming of the soft and hard thresholding methods, a new thresholding function was constructed by combining the sofe and had thresholding methods and used to denoising on the low light level image. This method has good continuity and convenient adjustment, the final image SNR has been further improved, and has higher application value. The PSNR is37.49,44.76and44.81for the frame integral method, hard threshold method and soft threshold methed, respectively. While the PSNR is45.87for the algorithm adopted in this paper.
引文
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