# Discriminative Transfer Learning for General Image Restoration

## TL;DR

This paper introduces a discriminative transfer learning approach for general image restoration that enables a single trained model to handle multiple tasks and conditions efficiently, with easy transferability to new problems.

## Contribution

The proposed method combines proximal optimization with discriminative learning, allowing one-pass training and transferability across various image restoration tasks and conditions.

## Key findings

- Requires only a single training pass
- Can be transferred to untrained tasks
- Achieves efficiency comparable to existing methods

## Abstract

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.