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Put your name and matric number <3

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Component

1 Axes

Image axes

8 Button

Normal image

Gray scale

Median filtering

Image histogram

Threshold

Histogram equalization

Edge function

Hough Transform

2 Edit field (text)

Name

Matric

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v. Adjusting intensity value using histogram equalization

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v. Adjusting intensity value using histogram equalization

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vii. Detecting line using hough transform

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vii. Detecting line using hough transform

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% Output image 1: Filling all holesfigure;filledImage = imfill(ms, 'holes');imshow(filledImage);title('Filling all holes');

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% Output image 2: Show all circular objectsfigure;[L, num] = bwlabel(filledImage);stats1 = regionprops(filledImage, 'Area', 'Perimeter');circularObjects = f

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3)Output image 2 (Show all circular objects)

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3)Output image 2 (Show all circular objects)

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% Output image 3: Show all circular objects with and without holesfigure;circularObjectsWithHoles = ms .* circularObjects;imshow(circularObjectsWithHoles);t

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4)Output image 3 (Show all circular objects with and w/o holes) 

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4)Output image 3 (Show all circular objects with and w/o holes) 

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% Output image 4: Show all rectangular objectsfigure;rectangularObjects = filledImage - circularObjects;[ml, kl] = bwlabel(rectangularObjects);imshow(rectan

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5)Output image 4 (Show all rectangular objects) 

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5)Output image 4 (Show all rectangular objects) 

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% Output image 5: Show all rectangular objects with and without holesfigure;rectangularObjectsWithHoles = ms .* rectangularObjects;imshow(rectangularObjectsW

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6)Output image 5 (Show all rectangular objects with and w/o holes) 

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6)Output image 5 (Show all rectangular objects with and w/o holes) 

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i. Understand the CAN - the multiscale context aggregation network.

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i. Understand the CAN - the multiscale context aggregation network.

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a. How many training images are used in the example? What is the size of the input image?

300, 000 images(20000 images x 15 epoch) with size of images 256-by-256 pixels.

b. There are two types of images used for the training process. What are these images?

pristineImages and bilatFilteredImages.

c. What are the inputs of the network? What are the desired responses of the network?

randomPatchExtractionDatastore

d. What are the layers used in the multiscale CAN from the Deep Learning Toolbox™?

adaptiveNormalizationMu — Scale layer that adjusts the strengths of the batch-normalization branch

adaptiveNormalizationLambda — Scale layer that adjusts the strengths of the identity branch

e. What is the first layer, middle layer, and the last layer of the CAN network of the example?

The first layer is image Input Layer that can operates on image patches. For the second layer is 2-D convolution layer. The last layer is a regression layer instead of a leaky ReLU layer.

f. What is the training algorithm used to train the network? What are the default values of the hyperparameter of the network?

Adam optimizer.

default values of 0.9 for "Momentum" and 0.0001 for "L2Regularization" (weight decay).

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The missing component are as follows:

1. Conv3-BN3-Mu3-add3/in1-Leaky3-Conv4

2. Conv4-BN4-Mu4-add4/in1-Leaky2-Conv5

3. Conv5-BN5-Mu5-add5/in1-Leaky2-Conv6

4. Conv6-BN6-Mu6-add6/in1-Leaky6-Conv7

5. Conv7-BN7-Mu7-add7/in1-Leaky2-Conv8

6. Conv8-BN8-Mu8-add8/in1-Leaky8-Conv9

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Gaussian White Noise with code noisyImg = imnoise (img, 'gaussian');

imshow(noisyImg);

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Bilateral Filtering with code : filteredImg = imbilatfilt(img);

imshow(filteredImg);

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denoiseImage = predict(net, noisyI);

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denoiseImage = predict(net, noisyI);