Up-sampling 4D images using deep learning

To have a deep learning network that accepts 4D low-resolution images and produces 4D high-resolution images.

Project Objectives:

1- Get the 4D low-resolution images through running a degradation model on the ground-truth 4D images dataset by estimating various blur kernels as well as real noise distributions. Most current approaches rely on paired low and high-resolution images to train the network in a fully supervised manner. However, such image pairs are not available in real-world applications. Thus, the aim here is instead to learn super-resolution from unpaired data and without any restricting assumptions on the input image formation.

2- Use GAN (Generative Adversarial Network) framework to perform the up-sampling within the network using transposed convolutions. The aim here is for the network to take “low-spatial-resolution images (for example 512x512) produced from objective 1” as input and predicts “high-spatial-resolution images (for example 1024x1024)” as an output in a coarse-to-fine fashion. The GAN framework should learn the correlations between LR and HR 4D volume of images using the following Loss functions: pixel loss, perceptual loss, and adversarial loss.

I have some written codes (implemented but not trained) that perform the following:

A degradation model that takes 4D light images as input and produces Downsampled (degraded) 4D light field images.

A GAN (Generative Adversarial Network) framework that accepts 4D light field dataset and learns the correlations between LR and HR 4D volume of images.

A high dimensional (4D) residual network that performs the up-sampling within the network using transposed convolutions of 4D images dataset.

Evner: Python, Machine Learning (ML), Deep Learning

About the Client:
( 0 bedømmelser ) Santa Clara, United States

Projekt ID: #32667315

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