- Apply naive deconvolution, Wiener deconvolution on blurred in frequency domain
- Estimate frequency-dependent Signal-Noise Ratio (SNR) by analyzing the power spectral density of images
- Experiment with different approximation of SNR function for Wiener Filter algorithm
- Evaluate quantitatively the performance of different deconvolution algorithms
- Use Orthogonal Matching Pursuit (OMP) to solve sparse coding problem
- Learn sparse dictionary using K-SVD algorithm
- Implement these two algorithms for an image inpainting task, which aims to remove extra text content on an image
- Evaluate quantitatively the reconstruction performance
- Implement data augmentation methods when training a convolutional neural networks (CNN) on images
- Design a CNN pipeline for multi-class image classification task with 6 layers, which is only consisted of convolutional layers and fully-connected layers
- User rectified linear unit (ReLU) as the activation function and cross entropy loss as the loss function
- Evaluate the classification performance