Next Article in Journal
Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications
Next Article in Special Issue
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
Previous Article in Journal
Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order
Previous Article in Special Issue
Use of an Occlusion Mask for Veiling Glare Removal in HDR Images

An Uncertainty-Aware Visual System for Image Pre-Processing

Computer Graphics and HCI Group, University of Kaiserslautern, 67663 Kaiserslautern, Germany
Department of Biomedical Engineering, Universidad de los Andes, Cra 1 Este No 19A-40 Bogota, Colombia
IMAGINE Group, Universidad de los Andes, Cra 1 Este No 19A-40 Bogota, Colombia
Advanced Visual Data Analysis, Wright State University, OH 45435, USA
Author to whom correspondence should be addressed.
J. Imaging 2018, 4(9), 109;
Received: 13 August 2018 / Revised: 3 September 2018 / Accepted: 5 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue Image Enhancement, Modeling and Visualization)
Due to image reconstruction process of all image capturing methods, image data is inherently affected by uncertainty. This is caused by the underlying image reconstruction model, that is not capable to map all physical properties in its entirety. In order to be aware of these effects, image uncertainty needs to be quantified and propagated along the entire image processing pipeline. In classical image processing methodologies, pre-processing algorithms do not consider this information. Therefore, this paper presents an uncertainty-aware image pre-processing paradigm, that is aware of the input image’s uncertainty and propagates it trough the entire pipeline. To accomplish this, we utilize rules for transformation and propagation of uncertainty to incorporate this additional information with a variety of operations. Resulting from this, we are able to adapt prominent image pre-processing algorithms such that they consider the input images uncertainty. Furthermore, we allow the composition of arbitrary image pre-processing pipelines and visually encode the accumulated uncertainty throughout this pipeline. The effectiveness of the demonstrated approach is shown by creating image pre-processing pipelines for a variety of real world datasets. View Full-Text
Keywords: image pre-processing; uncertainty quantification; uncertainty propagation image pre-processing; uncertainty quantification; uncertainty propagation
Show Figures

Figure 1

MDPI and ACS Style

Gillmann, C.; Arbelaez, P.; Hernandez, J.T.; Hagen, H.; Wischgoll, T. An Uncertainty-Aware Visual System for Image Pre-Processing. J. Imaging 2018, 4, 109.

AMA Style

Gillmann C, Arbelaez P, Hernandez JT, Hagen H, Wischgoll T. An Uncertainty-Aware Visual System for Image Pre-Processing. Journal of Imaging. 2018; 4(9):109.

Chicago/Turabian Style

Gillmann, Christina, Pablo Arbelaez, Jose T. Hernandez, Hans Hagen, and Thomas Wischgoll. 2018. "An Uncertainty-Aware Visual System for Image Pre-Processing" Journal of Imaging 4, no. 9: 109.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop