This project is an unsupervised image restoration tool that uses a convolutional neural network as a structural prior to reconstruct images from noisy or incomplete data. It functions as a neural network image prior, utilizing the inherent biases of the network architecture to restore pixels without the need for a pre-trained dataset or external learning.
The system performs zero-shot image restoration by treating the network architecture itself as a regularization term. It uses a randomly initialized encoder-decoder structure and iterative gradient descent to minimize pixel-wise loss, recovering the natural image structure through self-supervised reconstruction.
The tool covers several image recovery domains, including image denoising, super-resolution, and general image restoration. It also provides capabilities for blind image deconvolution to recover sharp images from blurred versions.
