Difference between revisions of "General Image Processing"
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== [[Noise reduction]] == | == [[Noise reduction]] == | ||
| − | + | In order to deal the image including a lot of noise, knowing well the nature of the noise is important. | |
| − | + | Show as following some factors about noise related to electron microscope. | |
| − | # | + | # Quantum noise by lack of electron dose: White Noise(Whole field) |
| − | # | + | # Noise which exists many in the low-resolution side by energy lack of electron or chromatic aberration: Colored Noise(Whole field) |
| − | # | + | # Noise by radiation or dust on CCD, fluorescent screen, or film of camera: Local Noise |
| − | + | and so on. | |
| − | + | Quantum Noise is one kind of White noise, and it is known that this is often noise distribution depending on Poisson Process. | |
| − | + | In the low-resolution side There is noise by Inelastic scattered electrons or chromatic aberration, with blur. Therefor, this noise is Colored. | |
| − | + | Noise by radiation or dust on CCD, fluorescent screen, or film of camera is one example of Local Noise. Factor by cosmic rays or radiation by collapse of the fluorescent screen gives pixels which have considerable high contrast. Because of [[MTF]](e.g. by CCD), it is not one point, but occurs blur. | |
=== [[Smoothing]] === | === [[Smoothing]] === | ||
| − | + | This is an image processing method with the primary purpose of eliminating the noise in the image. By understanding well the nature of the noise, the noise can be reduced properly. | |
== [[Edge extraction]] == | == [[Edge extraction]] == | ||
Revision as of 01:51, 2 October 2014
On Genaral Image Processing, describe about general foundation of image processing with some examples by using Eos.
Contents
- 1 Foundation of image processingとSimple Image Processing
- 2 Input of image and Lens
- 3 CTF,PSF and MTF
- 4 Sampling
- 5 Quantization
- 6 Noise reduction
- 7 Edge extraction
- 8 Binarization
- 9 Fourier Spaceを利用したImage Processing
- 10 実空間のKernelを用いたImage Processing
- 11 Mathematical morphologyを使ったImage Processing
- 12 Image Averaging
Foundation of image processingとSimple Image Processing
Tutorial on simple image processing using the Eos has been published.
Input of image and Lens
Before performing image processing using a computer, you should keep in mind about the input device that digitize images.Then, it is important to understand optical element called lens which creates image by condensing for light occurred at object.
CTF,PSF and MTF
True image: f(x,y) is degraded due to the method of Input of image and performance of the Lens.こThen, It might occur uniform blur(degradation) at whole field. Here, the function about degradation of original point image is called PSF(Point Spread Function). If this Point Spread Function PSF(x, y) is determined, it can be regarded that an observed image g(x, y) is convoluted PSF(x, y) to a true image f(x, y).
Sampling
To convert analog images to digital images, it is important that discretize the space by decomposing. This step is called Sampling. This failure might make incorrect interpretation like false image. Therefore, you must process carefully.
Quantization
First, in digital image process the operation that expresses density values within certain number of bits (Optical density) is required. This operation is called quantization(AD convert). Information lost by this process can't be regained.
Noise reduction
In order to deal the image including a lot of noise, knowing well the nature of the noise is important. Show as following some factors about noise related to electron microscope.
- Quantum noise by lack of electron dose: White Noise(Whole field)
- Noise which exists many in the low-resolution side by energy lack of electron or chromatic aberration: Colored Noise(Whole field)
- Noise by radiation or dust on CCD, fluorescent screen, or film of camera: Local Noise
and so on.
Quantum Noise is one kind of White noise, and it is known that this is often noise distribution depending on Poisson Process.
In the low-resolution side There is noise by Inelastic scattered electrons or chromatic aberration, with blur. Therefor, this noise is Colored.
Noise by radiation or dust on CCD, fluorescent screen, or film of camera is one example of Local Noise. Factor by cosmic rays or radiation by collapse of the fluorescent screen gives pixels which have considerable high contrast. Because of MTF(e.g. by CCD), it is not one point, but occurs blur.
Smoothing
This is an image processing method with the primary purpose of eliminating the noise in the image. By understanding well the nature of the noise, the noise can be reduced properly.
Edge extraction
対象物の形を理解するために重要なステップですが,とても難しいステップでもあります.
Binarization
Signalと背景の切り分けや,代表点や骨格の抽出など,Image ProcessingやAnalysisのスタートとなる処理方法です.
Fourier Spaceを利用したImage Processing
繰り返し周期がある画像などでは有効な画像小胞です.
実空間のKernelを用いたImage Processing
Mathematical morphologyを使ったImage Processing
Image Averaging
the same photo fieldや同一粒子's imageがたくさんある場合には,画像の平均化を行うことで画像の質を上げることができます．
