Deep Model-Based Super-Resolution with Non-uniform Blur

• Main Authors: Laroche, Charles, Almansa, Andres, Tassano, Matias
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms: Computational photography; Computer vision; image and video synthesis; Image restoration; Iterative methods; Kernel; Noise measurement; Superresolution; Training
• ISSN: 2642-9381
• DOI: 10.1109/WACV56688.2023.00184

We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsampled, and noisy measurements. Despite their impressive performance, existing techniques usually assume a uniform blur kernel. Hence, these techniques do not generalize well to the more general case of non-uniform blur. Instead, in this paper, we address the more realistic and computationally challenging case of spatially-varying blur. To this end, we first propose a fast deep plug-and-play algorithm, based on linearized ADMM splitting techniques, which can solve the super-resolution problem with spatially-varying blur. Second, we unfold our iterative algorithm into a single network and train it end-to-end. In this way, we overcome the intricacy of manually tuning the parameters involved in the optimization scheme. Our algorithm presents remarkable performance and generalizes well after a single training to a large family of spatially-varying blur kernels, noise levels and scale factors.