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The number of training images used is determined by the command numImages = numel(imds.Files). The size of the input image is [256 256 3].

There are two types of images used for the training process: clean images and noisy images.

The inputs to the network are noisy images, while the desired responses of the network are the corresponding clean images.

The layers used in the multiscale CAN (Convolutional Autoencoder Network) from the Deep Learning Toolbox™ include the Input Layer, Convolutional Layers, Batch Normalization Layers, and Activation Layer.

The first layer of the CAN network is the Input Layer, the middle layer is a Convolutional Layer, and the last layer is an Activation Layer.

The training optimizer used to train the network is Adam. The default values of the network's hyperparameters are as follows:

Learning Rate: 0.001 (initial)

MiniBatchSize: 32

MaxEpochs: 10

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NUR SYAFISA QHAIRUNNISA (87166)

SITI FAZIRAH (86726)

NURUL HAYATI (85372)

ANIS NATASHA (83258)

MUHAMMAD SYAHZARULFADHIL (84758)

MOHAMMAD NADZMEE AZHAD

(84578)

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Adjusting Contrast Using Histogram Equalization

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Adjusting Contrast Using Histogram Equalization

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Compute the Magnitude of The Gradient Using Sobel

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Compute the Magnitude of The Gradient Using Sobel

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Compute the Magnitude of The Gradient Using Prewitt

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Compute the Magnitude of The Gradient Using Prewitt

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Compute the Magnitude of The Gradient Using Gradient

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Compute the Magnitude of The Gradient Using Gradient

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Detecting Lines Using the Hough Transform

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Detecting Lines Using the Hough Transform

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Pattern Finding in Image Using Cross-Correlation

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Pattern Finding in Image Using Cross-Correlation

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What are the problem or limitation faced in this part of the project?

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What are the problem or limitation faced in this part of the project?

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Issue with Picture Placement on Axes

There was an error when trying to insert or overlay a picture onto the axes of a graph or chart. This could refer to difficulties aligning the picture, scaling it appropriately, or formatting it to fit correctly within the plot.

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Our group think that this project aims to achieve robust, scalable, and accurate detection for applications such as industrial inspection, medical imaging, and object recognition.

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Noise images

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Denoised images obtained using multiscale CAN

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For part C, our group think that this project aims to demonstrate the power of deep learning in solving complex image processing challenges, with potential applications in fields such as healthcare, autonomous driving, and industrial automation.

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What are the problems or limitations faced in this part of the project?

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What are the problems or limitations faced in this part of the project?

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1. Spend more time learning and understanding how to work on the CAN (Convolutional Autoencoder Network) part, as it can be a bit complicated.

2. Faced hardware issues because the program is large and needs a lot of processing power to run properly.

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What are the problem or limitation faced in this part of the project?

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What are the problem or limitation faced in this part of the project?

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Some picture error to input in the axes picture