Kim, Kwang-Yong
(Spatial Imaginary Information Research Team, Electronics and Telecommunications Research Institute)
,
Jeong, Soo
(Spatial Imaginary Information Research Team, Electronics and Telecommunications Research Institute)
,
Kim, Kyung-Ok
(Spatial Imaginary Information Research Team, Electronics and Telecommunications Research Institute)
Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle...
Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle noise in SAR images is modeled to be multiplicative, and therefore, a signal-dependent noise. So, it has deflated many image-denoising algorithms that are based on additive noise model. In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail. We first decompose minutely the high frequency level to analyze the noise level. And then, we determine the weighting threshold value per the level, and layer. Finally, using those weighting threshold, we produce the efficient wavelet shrinkage method. So, this method not only reduces the speckle noise, but also preserves image detail and sharpness.
Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle noise in SAR images is modeled to be multiplicative, and therefore, a signal-dependent noise. So, it has deflated many image-denoising algorithms that are based on additive noise model. In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail. We first decompose minutely the high frequency level to analyze the noise level. And then, we determine the weighting threshold value per the level, and layer. Finally, using those weighting threshold, we produce the efficient wavelet shrinkage method. So, this method not only reduces the speckle noise, but also preserves image detail and sharpness.
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
제안 방법
But, this test synthesized the image until 2 level, and procedured the proposed method on only 1 level, for testing the performance in high frequency information. And then we congared the image by the proposed image with that by the general wavelet shrinkage techniques, and original image.
In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail.
In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by considering for the spectral characteristics between wavelet decon^osition levels.
대상 데이터
Test image was formed by various configuration of the ground like a river, rice fields, and mountains, and had much speckle noises.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.