Neural Networks Project

Andrea Mazzitelli - 1835022 - mazzitelli.1835022@studenti.uniroma1.it

Andrea Rodriguez - 1834937 - rodriguez.1834937@studenti.uniroma1.it

Introduction

This repository contains our Neural Networks Project in which we try to replicate the results obtained in the paper: Zero Shot Image Restoration Using Denoising Diffusion Null-Space Model (Wang et al., 2022).

The paper introduces a new zero-shot approach to solve Image Restoration (IR) tasks starting from a generic pre-trained diffusion model. The authors propose a sampling algorithm called DDNM and an enhanced version DDNM+. The method presented is Zero Shot because it can perform any IR task without needing a model explicitly trained for the task. Only a matrix representing the transformation to be done must be chosen according to the task to be performed.

In this repository you can find:

• the images used for testing;
• a pdf report containing an explanation of the method, our approach, evaluation of the results and an image gallery;
• a notebook containing the information of the report together with the code to be run;
• some introductory slides (link).

Instructions

To test our code open the colab, connect to a runtime with GPU and follow these instruction:

• If the colab is being executed for the first time:

  • connect to your google drive only if you want to upload custom images or masks
  • choose the parameters in the form and lauch that cell
  • run all the cells up to Algorithm implementation (it may take some minutes to download the pre-trained models)
  • choose the algorithm to use and run the corresponding cell in the Execution block
  • wait for the results that will be shown compared to the input image

• If you already run any algorithm and want to run again with some changes in the parametes:

  • change the parameters (the cell should run automatically)
  • if the changes on the parameters involved in some capacity the image or the mask (for example: changed image, added/removed the mask), run again the cell with header "Image and Mask Setup"
  • choose the algorithm to use and run the corresponding cell in the Execution block
  • wait for the results that will be shown compared to the input image

• If you want to change the model (ImageNet or CelebA):

  • change the parameters (the cell should run automatically)
  • run again the cell with header "Model Setup"
  • choose the algorithm to use and run the corresponding cell in the Execution block
  • wait for the results that will be shown compared to the input image