2013
CVPR
CVPR 2013
A Convex Regularizer for Reducing Color Artifact in Color Image Recovery
Abstract
We propose a new convex regularizer, named the local color nuclear norm (LCNN), for color image recovery. The LCNN is designed to promote a property inherent in natural color images - in which their local color distributions often exhibit strong linearity - and is thus expected to reduce color artifact effectively. In addition, the very nature of LCNN allows us to incorporate it into various types of color image recovery formulations, with the associated convex optimization problems solvable using proximal splitting techniques. Applicatinos of LCNN are demonstrated with illustrative numerical examples.
🚀
Conference Pioneer
— CVPR 2013
🌉
Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning and Mathematics & Optimization
🧭
Keyword Pioneer
— convex regularizer
🐣
Hot Topic Early Bird
— image processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio