Convex Variational Methods and Optimization Techniques for Image Processing

Show simple item record

dc.contributor Coll Vicens, Bartomeu
dc.contributor Duran Grimalt, Joan Oliver Bonafoux, Ramon 2018 2020-03-24T10:25:36Z 2020-03-24T10:25:36Z
dc.description.abstract [eng] Image processing problems have emerged as essential in our society. Indeed, in a world where technology and computers play a central role, images are always present in our everyday life. Therefore, the necessity of improving the quality of certain images, such as medical images or remote sensing images, has called the attention of contemporary mathematics. Several mathematical techniques can be used in order to process an image. We analyze the basics on variational methods for image processing problems, from the Calculus of Variations to Convex Analysis and Continuous Optimization. These items are preceded by a general overview on Functional Analysis topics. Lastly, we consider the problems of denoising and conditioned interpolation of digital images. We will propose several variational models to solve them and perform several experimental results to compare their efficiency.
dc.format application/pdf
dc.language.iso eng
dc.publisher Universitat de les Illes Balears
dc.rights al rights reserved
dc.rights info:eu-repo/semantics/openAccess
dc.subject 51 – Matemàtiques
dc.title Convex Variational Methods and Optimization Techniques for Image Processing
dc.type info:eu-repo/semantics/bachelorThesis
dc.type info:eu-repo/semantics/publishedVersion

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository

Advanced Search


My Account