Robust image filtering using joint static and dynamic guidance

Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered structures. One o...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 4823 - 4831
Hauptverfasser: Ham, Bumsub, Minsu Cho, Ponce, Jean
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2015
Schlagworte:
Algorithms
Color
Computer vision
Filtering
Filtration
Handles
Image edge detection
Image resolution
Joints
Kernel
Linear programming
Noise reduction
Texture
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered structures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input images. We address this problem by jointly leveraging structural information of guidance and input images. Image filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. It effectively controls image structures at different scales and can handle a variety of types of data from different sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super-resolution, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2015.7299115