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Here, here and here. Monday, October 12, 2009. ACTIVITY 19 - Restoration of Blurred Image. This activity is a demonstration on how to restore an image which is corrupted with a known degradation function such as motion blur and additive noise. The image degradation and restoration process can be modeled by the diagram shown below. A degradation function H. Along with an additive noise terms, n(x,y). Operates on an input image f(x,y). Producing a degraded image g(x,y). And additive noise terms n(x,y).

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here, here and here... | ghacoeurei.blogspot.com Reviews

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Here, here and here. Monday, October 12, 2009. ACTIVITY 19 - Restoration of Blurred Image. This activity is a demonstration on how to restore an image which is corrupted with a known degradation function such as motion blur and additive noise. The image degradation and restoration process can be modeled by the diagram shown below. A degradation function H. Along with an additive noise terms, n(x,y). Operates on an input image f(x,y). Producing a degraded image g(x,y). And additive noise terms n(x,y).

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1 where k

2 1 and a

3 and b

4 by keeping a

5 constant t

6 scf 1 ;

7 weiner filtering

8 scf 2 ;

9 posted by

10 ghary

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here, here and here... | ghacoeurei.blogspot.com Reviews

https://ghacoeurei.blogspot.com

Here, here and here. Monday, October 12, 2009. ACTIVITY 19 - Restoration of Blurred Image. This activity is a demonstration on how to restore an image which is corrupted with a known degradation function such as motion blur and additive noise. The image degradation and restoration process can be modeled by the diagram shown below. A degradation function H. Along with an additive noise terms, n(x,y). Operates on an input image f(x,y). Producing a degraded image g(x,y). And additive noise terms n(x,y).

INTERNAL PAGES

ghacoeurei.blogspot.com

1

here, here and here...: ACTIVITY 16 - Neural Networks

http://ghacoeurei.blogspot.com/2009/09/activity-16-neural-networks.html

Here, here and here. Wednesday, September 9, 2009. ACTIVITY 16 - Neural Networks. This activity is again related from the two previous activities, Activity 14 and 15. The purpose of this activity is to classify objects from their corresponding class using neural networks. The features of of the samples from the two classes used in Activity 15 was also used in this activity. The Clorets. Mint candy was tagged with the value of 0 while the Pillows. Chocolate snack was tagged with the value of 1. The traini...

2

here, here and here...: June 2009

http://ghacoeurei.blogspot.com/2009_06_01_archive.html

Here, here and here. Sunday, June 28, 2009. ACTIVITY 2 - Area Estimation of Images with Defined Edges. In this activity, our goal is to be able to compute for the area of images with the use of Green's Theorem. First, we created images (with black background and white as the object) using MS Paint. Then, the areas of the object on each image were calculated using Green's equation given by. I create a code using scilab. Image = imread('tri 4900.JPG');/ read. X,y] = follow(bin);/ follow the co. Disregard t...

3

here, here and here...: ACTIVITY 19 - Restoration of Blurred Image

http://ghacoeurei.blogspot.com/2009/10/activity-19-restoration-of-blurred.html

Here, here and here. Monday, October 12, 2009. ACTIVITY 19 - Restoration of Blurred Image. This activity is a demonstration on how to restore an image which is corrupted with a known degradation function such as motion blur and additive noise. The image degradation and restoration process can be modeled by the diagram shown below. A degradation function H. Along with an additive noise terms, n(x,y). Operates on an input image f(x,y). Producing a degraded image g(x,y). And additive noise terms n(x,y).

4

here, here and here...: July 2009

http://ghacoeurei.blogspot.com/2009_07_01_archive.html

Here, here and here. Monday, July 13, 2009. ACTIVITY 6 - Properties of the 2D Fourier Transform. Activity 6.A Familiarization with FT of Different 2D Patterns. This activity is similar to the previous one. We created different patterns, took their respective Fourier Transform and then display their FT modulus. This patterns were done with the aid of MS. Made it possible to compute their Fourier Transform. Next is a two-slit pattern. Along its side is its FT which yields from the cancellation of the u...

5

here, here and here...: ACTIVITY 17 - Photometric Stereo

http://ghacoeurei.blogspot.com/2009/09/activity-17-photometric-stereo.html

Here, here and here. Wednesday, September 9, 2009. ACTIVITY 17 - Photometric Stereo. In this activity, we estimated and then extracted the shape of an object from shadow with the use of different sources and shading models. First, we utilized the images of of synthetic spherical surfaces that are illuminated by a far away point source. This numbers were put into matrix form where each row is a source and each column is the x,y, and z component of the source. With the use of the equation. The shape of the...

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jaysamuel-ap186.blogspot.com

Pure awesomeness..: July 2009

http://jaysamuel-ap186.blogspot.com/2009_07_01_archive.html

Ask and it will be given. Activity 9: Binary Operations. Activity 8 - Morphological Operations. Activity 7: Enhancement in the Frequency Domain. Activity 6 - Properties of the 2D Fourier Transfor. Activity 5 - Fourier Transform Model of Image Form. View my complete profile. Wednesday, July 29, 2009. Activity 9: Binary Operations. Figure 1. Grayscale image of ' cells'. Figure2. Thresholded sub-images in Fig.1. Figure 3. Structuring element used for morphological operations. To label the blobs in each sub-...

jaysamuel-ap186.blogspot.com

Pure awesomeness..: September 2009

http://jaysamuel-ap186.blogspot.com/2009_09_01_archive.html

Ask and it will be given. Activity 18 - Noise model and basic image restorat. Activity 17 - Photometric Stereo. Activity 16 - Neural Networks. Activity 15 - Probabilistic Classification. View my complete profile. Thursday, September 24, 2009. Activity 18 - Noise model and basic image restoration. Impulse (salt an pepper) Noise:. For z = a. For z = b. The figure below demonstrates the effect of each noise PDF to an image. Here we initially observe a grainy effect of the noise. In an image with center x.

jrvitug.blogspot.com

Chocolate lover: July 2011

http://jrvitug.blogspot.com/2011_07_01_archive.html

Wednesday, July 13, 2011. Even if everybody thinks you're not. I still believe that you are". We are human beings. not perfect. but we shouldn't be deprived of the chance to improve ourselves and show the society the better side of us. Just some random thoughts from a very lazy evening. I remember the time when I last spoke about this. Subscribe to: Posts (Atom).

jrvitug.blogspot.com

Chocolate lover: Happy weekend ^_^

http://jrvitug.blogspot.com/2011/06/happy-weekend.html

Friday, June 17, 2011. And so my day ended very happily! I know God will not let things happen without lessons to be learned. I will always trust in You Lord. What a happy weekend indeed! Subscribe to: Post Comments (Atom). I was just so scared.

ccesporlas-ap186.blogspot.com

CINDY 186: Activity 18 | Noise Models and Basic Image Restoration

http://ccesporlas-ap186.blogspot.com/2009/10/activity-18-noise-models-and-basic.html

Monday, October 5, 2009. Activity 18 Noise Models and Basic Image Restoration. Image restoration is a basic image processing application where an image that has been degraded or damaged is being recovered. To recover the image, the degradation must be known and thus can be used in restoring the image. Figure 1. PDFs of different noise types. Figure 2. Types of filtering used. Figure 3. Test image and its PDF. Figure 4 shows the noisy image for the added Gaussian noise with mean = 0.5 and standard dev...

ccesporlas-ap186.blogspot.com

CINDY 186: Activity 17 | Photometric Stereo

http://ccesporlas-ap186.blogspot.com/2009/09/activity-17-photometric-stereo.html

Wednesday, September 9, 2009. Activity 17 Photometric Stereo. Figure 1. Photometric Stereo. Equation 1. Intensity at point P(x,y). In this activity, we use four images of an object taken with different light source positions and use it to reconstruct the 3D structure of the illuminated area, as shown by Figure 2. Figure 2. Images of object at different light source positions. To calculate for the surface normal of the object we need to get the far away point source locations given by V. Buno, Luis III.

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ghacoeurei.blogspot.com

here, here and here...

Here, here and here. Monday, October 12, 2009. ACTIVITY 19 - Restoration of Blurred Image. This activity is a demonstration on how to restore an image which is corrupted with a known degradation function such as motion blur and additive noise. The image degradation and restoration process can be modeled by the diagram shown below. A degradation function H. Along with an additive noise terms, n(x,y). Operates on an input image f(x,y). Producing a degraded image g(x,y). And additive noise terms n(x,y).

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