Abstract
We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (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. A despeckling evaluation index (DEI) 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 different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.
Highlights
S YNTHETIC aperture radar (SAR) is an important technology for various remote sensing applications such as Earth environment monitoring, geohazard investigation, etc., with the capability of all-weather and all-time observation
This paper presents a synthetic aperture radar (SAR) image despeckling method based on adaptive total variation (TV) (ATV) regularization
To assess the performance of edge preserve despeckling for SAR images, a despeckling evaluation index (DEI) is designed based on a ratio between the standard deviation over a small neighborhood and that over a larger neighborhood, as defined in the following: 1 DEI = N
Summary
S YNTHETIC aperture radar (SAR) is an important technology for various remote sensing applications such as Earth environment monitoring, geohazard investigation, etc., with the capability of all-weather and all-time observation. The Lee [2], Frost [3], Kuan [4], and adaptive median filters are typical examples of such techniques These conditional low-pass filtering based techniques are simple and have high processing efficiency but show limitations on either preserving image sharpness or effective speckle suppression, and the performance is scene dependent. The total variation (TV) regularization based image restoration model was introduced by Rudin, Osher, and Fatemi [14], and it has raised wide research interests in digital image processing and computer vision It has a good performance in preserving image sharpness and edges while removing noises. This paper presents a SAR image despeckling method based on adaptive TV (ATV) regularization. The experimental results show that the proposed ATV method can effectively suppress the speckles of SAR image with good preservation of the sharpness of edges as confirmed by both the DEI and visual assessment.
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