Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavel...Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.展开更多
In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary...In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.展开更多
散斑噪声作为超声图像的主要噪声严重影响超声成像质量,滤除散斑噪声是超声图像处理过程中重要步骤。以曲波阈值去噪方法为基础,针对常用阈值函数中对较小曲波系数处理粗糙、不连续、收敛慢的缺点,通过分析实际超声图像中散斑噪声的分布...散斑噪声作为超声图像的主要噪声严重影响超声成像质量,滤除散斑噪声是超声图像处理过程中重要步骤。以曲波阈值去噪方法为基础,针对常用阈值函数中对较小曲波系数处理粗糙、不连续、收敛慢的缺点,通过分析实际超声图像中散斑噪声的分布,提出了具有与实际噪声分布相关特点的曲波阈值去噪方法。对比测试实验结果表明,曲波去噪方法相比其他去噪方法在不同噪声水平下均具有更加稳定优异的去噪性能,峰值性噪比提高了1~2 d B,平均结构相识度相比也有较大的提高。展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant No. 60472048.
文摘Synthetic aperture radar （SAR） images are corrupted by multiplicative speckle noise which limits the performance of the classical coder/decoder algorithm in spatial domain. The relatively new transform of multiwavelets can possess desirable features simultaneously, such as orthogonality and symmetry, while scalar wavelets cannot. In this paper we propose a compression scheme combining with speckle noise reduction within the multiwavelet framework. Compared with classical set partitioning in hierarchical trees （SPIHT） algorithm, our method achieves favorable peak signal to noise ratio （PSNR） and superior speckle noise reduction performances.
文摘In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.
文摘散斑噪声作为超声图像的主要噪声严重影响超声成像质量,滤除散斑噪声是超声图像处理过程中重要步骤。以曲波阈值去噪方法为基础,针对常用阈值函数中对较小曲波系数处理粗糙、不连续、收敛慢的缺点,通过分析实际超声图像中散斑噪声的分布,提出了具有与实际噪声分布相关特点的曲波阈值去噪方法。对比测试实验结果表明,曲波去噪方法相比其他去噪方法在不同噪声水平下均具有更加稳定优异的去噪性能,峰值性噪比提高了1~2 d B,平均结构相识度相比也有较大的提高。
