Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot
  Image Restoration
Tomáš Chobola Gesine Müller Veit Dausmann Anton Theileis Jan Taucher Jan Huisken Tingying Peng
Non-blind deconvolution aims to restore a sharp image from its blurred
counterpart given an obtained kernel. Existing deep neural architectures are
often built based on large datasets of sharp ground truth images and trained
with supervision. Sharp, high quality ground truth images, however, are not
always available, especially for biomedical applications. This severely hampers
the applicability of current approaches in practice. In this paper, we propose
a novel non-blind deconvolution method that leverages the power of deep
learning and classic iterative deconvolution algorithms. Our approach combines
a pre-trained network to extract deep features from the input image with
iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot
optimisation process is employed to integrate the deconvolved features,
resulting in a high-quality reconstructed image. By performing the preliminary
reconstruction with the classic iterative deconvolution method, we can
effectively utilise a smaller network to produce the final image, thus
accelerating the reconstruction whilst reducing the demand for valuable
computational resources. Our method demonstrates significant improvements in
various real-world applications non-blind deconvolution tasks.