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Discriminative Transfer Learning for General Image Restoration

Discriminative Transfer Learning for General Image Restoration


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 discriminative 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.

Exisiting System:

A variety of models for natural image statistics have been explored in the past. Traditionally, models for gradient statistics including total variation (TV), have been a popular choice. Another line of works explores patch-based image statistics, either as per-patch sparse model or modeling non-local similarity between patches. These prior models are general in the sense that they can be applied for various likelihoods, with the image formation and noise setting as parameters. However, the resulting optimization problems are prohibitively expensive in many cases, rendering them impractical for many real-time tasks especially on mobile platforms.

Recently, a number of works have addressed this issue by truncating the iterative optimization and learning discriminative image priors, tailored to a specific reconstruction task (likelihood) and optimization approach.


Proposed System:

We propose a discriminative transfer learning technique for general image restoration. It requires a single-pass discriminative training and transfers across different restoration tasks and problem conditions.

We show that our approach is general by demonstrating its robustness for diverse low-level problems, such as denoising, deconvolution, inpainting, and for varying noise settings.

We show that, while being general, our method achieves comparable computational efficiency as previous discriminative approaches, making it suitable for processing high-resolution images on mobile imaging systems.

We show that our method can naturally be combined with existing likelihood terms and priors after being trained. This allows our method to process untrained restoration tasks and take advantage of previous successful work on image priors (e.g., color and non-local similarity priors).

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