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Image and Video Restorations via Nonlocal Kernel Regression
- Source :
- IEEE Transactions on Cybernetics. 43:1035-1046
- Publication Year :
- 2013
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2013.
-
Abstract
- A nonlocal kernel regression (NL-KR) model is presented in this paper for various image and video restoration tasks. The proposed method exploits both the nonlocal self-similarity and local structural regularity properties in natural images. The nonlocal self-similarity is based on the observation that image patches tend to repeat themselves in natural images and videos, and the local structural regularity observes that image patches have regular structures where accurate estimation of pixel values via regression is possible. By unifying both properties explicitly, the proposed NL-KR framework is more robust in image estimation, and the algorithm is applicable to various image and video restoration tasks. In this paper, we apply the proposed model to image and video denoising, deblurring, and superresolution reconstruction. Extensive experimental results on both single images and realistic video sequences demonstrate that the proposed framework performs favorably with previous works both qualitatively and quantitatively.
- Subjects :
- Video post-processing
Video Recording
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Pattern Recognition, Automated
Image texture
Artificial Intelligence
Image Interpretation, Computer-Assisted
Computer Science::Multimedia
Photography
Computer vision
Electrical and Electronic Engineering
Image restoration
Mathematics
Feature detection (computer vision)
business.industry
Binary image
Pattern recognition
Non-local means
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
Data Interpretation, Statistical
Subtraction Technique
Computer Science::Computer Vision and Pattern Recognition
Regression Analysis
Video denoising
Artificial intelligence
business
Algorithms
Software
Information Systems
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 43
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....5a1b085083977fc27c709f18779e5201