Name
Abstract | ||
|---|---|---|
In this paper, we introduce a total variation (TV) regularization model for SAR image despeckling. A dual formulation based adaptive total variation (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV regularization based image restoration model has a good performance in preserving image sharpness and edges while removing noises and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. An evaluation index is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of a pixel with different sizes. Experimental results show that the proposed method can effectively suppress SAR image speckles without compromise the edge sharpness of image features according to both subjective visual examination and objective evaluation indices of image quality. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IGARSS.2013.6723478 | IGARSS |
Keywords | Field | DocType |
artificial speckle,subjective visual examination,synthetic aperture radar,sar,image quality,edge preserve despeckling,dual formulation,image sharpness,optical image,optical images,image denoising,image restoration,image feature,sar image despeckling,image restoration model,tv regularization method,adaptive total variation regularization method,atv regularization method,adaptive total variation regularization,evaluation index,despeckling,radar imaging,indexes,tv,adaptive filters,noise,speckle | Computer vision,Speckle pattern,Computer science,Synthetic aperture radar,Feature (computer vision),Remote sensing,Image quality,Total variation denoising,Regularization (mathematics),Pixel,Artificial intelligence,Image restoration | Conference |
Volume | Issue | ISSN |
null | null | 2153-6996 |
ISBN | Citations | PageRank |
978-1-4799-1114-1 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Zhao | 1 | 32 | 5.94 |
| Jianguo Liu | 2 | 11 | 3.33 |
| bingchen zhang | 3 | 110 | 17.19 |
| Wen Hong | 4 | 355 | 49.85 |
| Yirong Wu | 5 | 396 | 46.55 |