Version 1
: Received: 31 May 2024 / Approved: 31 May 2024 / Online: 10 June 2024 (09:18:33 CEST)
How to cite:
Köhler, A.; Breuß, M.; Shabani, S. Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images. Preprints2024, 2024060355. https://doi.org/10.20944/preprints202406.0355.v1
Köhler, A.; Breuß, M.; Shabani, S. Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images. Preprints 2024, 2024060355. https://doi.org/10.20944/preprints202406.0355.v1
Köhler, A.; Breuß, M.; Shabani, S. Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images. Preprints2024, 2024060355. https://doi.org/10.20944/preprints202406.0355.v1
APA Style
Köhler, A., Breuß, M., & Shabani, S. (2024). Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images. Preprints. https://doi.org/10.20944/preprints202406.0355.v1
Chicago/Turabian Style
Köhler, A., Michael Breuß and Shima Shabani. 2024 "Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images" Preprints. https://doi.org/10.20944/preprints202406.0355.v1
Abstract
Image recovery by dictionary learning is of potential interest for many possibleapplications.To learn a dictionary, one needs to solve a minimization problem where the solutionshould be sparse. TheK-SVD formalism, which is a generalization of theK-means algorithm, is oneof the most popular methods to achieve this aim.We explain the preprocessing that is needed tobring images into a manageable format for the optimization problem.The learning process then takesplace in terms of solving for sparse representations of the image batches. The main contribution ofthis paper is to give an experimental analysis of the recovery for highly textured imagery. For ourstudy, we employ a subset of the Brodatz database. We show that the recovery of sharp edges playsa considerable role. Additionally, we study the effects of varying the number dictionary elements forthat purpose.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
