1. An Over-Complete Dictionary Design Based on GSR for SAR Image Despeckling
- Author
-
Su Liu, Yeo Tat Soon, and Gong Zhang
- Subjects
Synthetic aperture radar ,Structure (mathematical logic) ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0211 other engineering and technologies ,Wavelet transform ,Pattern recognition ,Speckle noise ,02 engineering and technology ,Sparse approximation ,Geotechnical Engineering and Engineering Geology ,Image (mathematics) ,Speckle pattern ,Radar imaging ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,021101 geological & geomatics engineering - Abstract
In this letter, we explore the concept of group sparse representation (GSR) to exploit the intrinsic structure of synthetic aperture radar (SAR) image. Noting that dictionary design is a crucial factor in GSR performance, we propose an over-complete dictionary to better fit the SAR image despeckling problem. This over-complete dictionary consists of the prespecified dictionaries and learned dictionary. Different kinds of dictionaries emulate the image from different angles. In this way, we can simultaneously obtain better performance on speckle noise suppression and image detail preservation. The experimental results on real SAR images demonstrate that the proposed over-complete dictionary based on GSR can achieve more effective speckle reduction as well as image detail preservation.
- Published
- 2017