# Image-Adaptive GAN based Reconstruction

## TL;DR

This paper proposes an image-adaptive GAN approach that improves image reconstruction quality by making generators more flexible and ensuring consistency with observations, demonstrated on super-resolution and compressed sensing tasks.

## Contribution

It introduces an image-adaptive GAN framework that enhances generative model flexibility and observation compliance for better inverse imaging problem solutions.

## Key findings

- Improved image reconstruction quality in super-resolution tasks.
- Enhanced compressed sensing results with the adaptive GAN approach.
- Demonstrated advantages over non-adaptive generative models.

## Abstract

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.