1 citations · 1 across the 3 of their papers we have counts for
3 papers
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Tomáš Chobola, Gesine Müller, Veit Dausmann +4
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 datas…
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Tomáš Chobola, Gesine Müller, Veit Dausmann +4
The process of acquiring microscopic images in life sciences often results in image degradation and corruption, characterised by the presence of noise and blur, which poses signifi…
DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior
Tomas Chobola, Anton Theileis, Jan Taucher +1
We present a model for non-blind image deconvolution that incorporates the classic iterative method into a deep learning application. Instead of using large over-parameterised gene…