Abstract
Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.
Highlights
Synthetic aperture radar (SAR) is a coherent imaging system [1]
Compared with the conventional the proposed algorithm achieves a moresuppression, accurate weighting weighting and homogeneous factorPPB, to improve the performance of speckle with a and homogeneous factor to improve the performance of speckle suppression, with a modified modified bias-reduction method to further balance speckle suppression with the correction of bright bias-reduction method to further balance speckle suppression with the correction of bright structure spreading
We developed a novel three-step technique based on the conventional probabilistic patch-based (PPB) algorithm
Summary
Synthetic aperture radar (SAR) is a coherent imaging system [1]. Each pixel in SAR images represents the coherent addition of scatterers from a corresponding resolution cell. This study proposes a novel speckle removal algorithm to suppress speckle noise and preserve edges and textures. Representative algorithms include Kuan [9] and maximum a posteriori (MAP) filtering [10] Such methods have been implemented in the spatial domain based on Bayesian criteria and a speckle model. Areas, techniques are often worsepreserve for de-noising and textures compared to the spatial filtering algorithms. These techniques are often worse for homogeneous areas than following approach. PDE-based approach gradually suppresses speckle noise during iterative processing and is sensitive to edge preservation. The non-local methods exploit similar pixels or blocks in images to implement filtering.
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