Hi xuan.
You can use the following steps as a general guidline:
- Prepare the Training Dataset: Create a dataset containing pairs of images, where one image is the original image, and the other image has missing pixels. Make sure you have enough training examples to cover a variety of missing pixel patterns.
- Preprocess the Data: Normalize the pixel values in the training images to a suitable range, such as [0, 1]. You can use functions like im2double or normalize in MATLAB for this purpose.
- Define the Neural Network Architecture: Design the architecture of your neural network. You can use MATLAB's Neural Network Toolbox or Deep Learning Toolbox to create and train neural networks. Consider using convolutional layers to capture local image features effectively.
- Split the Dataset: Split your dataset into training and validation sets. The training set will be used to update the network's weights, while the validation set will help monitor the network's performance during training and avoid overfitting.
- Train the Neural Network: Use the training set to train the neural network. You can use functions like trainNetwork or trainNetworkOptions in MATLAB to train your network. Configure the training options, such as the number of epochs, learning rate, and mini-batch size, based on your specific requirements.
- Evaluate the Network: After training, evaluate the performance of your network using the validation set. Calculate metrics like mean squared error (MSE) or peak signal-to-noise ratio (PSNR) to assess how well the network repairs the missing pixels.
- Test the Network: Finally, test your trained network on unseen images with missing pixels to see how well it performs in repairing them. You can use functions like predict to obtain the repaired images from your network.
