Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior

TL;DR

This paper introduces an unsupervised semi-blind image deblurring method using a dataset-free deep residual prior that adapts to various blurs and images, improving robustness to kernel errors.

Contribution

The paper proposes a novel dataset-free deep residual prior for kernel error modeling, integrating deep and hand-crafted priors for improved semi-blind deblurring.

Findings

Outperforms data-driven and model-driven methods in image quality

Demonstrates robustness to kernel error

Effective in real-world scenarios

Abstract

Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the latent image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to data-driven and model-driven methods in terms of image quality and the robustness to the kernel error.