1. Structure tensor adaptive total variation for image restoration
- Author
-
V. B. Surya Prasath and Dang Nh Thanh
- Subjects
General Computer Science ,business.industry ,Computer science ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Total variation denoising ,Structure tensor ,Noise ,Digital image processing ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering ,business ,Sensory cue ,Image restoration ,Eigenvalues and eigenvectors - Abstract
Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.
- Published
- 2019
- Full Text
- View/download PDF