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초록
In this paper, a deep convolutional neural network (CNN) is used to remove speckle noise from synthetic aperture radar (SAR) images. However, only applying CNN to remove noise causes an under-fitting problem. To overcome this issue, we suggest to use stationary wavelet transform (SWT) to the images as a pre-processing. Afterward, the resultant sub-band images are utilized to construct the similar sub-band images to the original images by training the CNNs. The training process is carried out by considering a large multi-temporal SAR image and its multi-look version. In the experiment result of this paper, the proposed method showed better performance compared to other denoising algorithms in regard to PSNR and SSIM.
키워드
Convolutional Neural Network; SAR; speckle noise; Stationary Wavelet Transform; Convolution; Deep neural networks; Image compression; Neural networks; Radar imaging; Speckle; Synthetic aperture radar; Convolutional neural network; De-noising algorithm; Deep convolutional neural networks; Fitting problems; Multi-temporal SAR images; Speckle noise; Stationary wavelet transforms; Synthetic aperture radar (SAR) images; Wavelet transforms
- 제목
- A despeckling method using stationary wavelet transform and convolutional neural network
- 저자
- Kim,Moonheum; Lee, Junghyun; Jeong, Je chang
- 발행일
- 2018-05
- 유형
- Conference Paper
- 저널명
- 2018 International Workshop on Advanced Image Technology, IWAIT 2018
- 페이지
- 1 ~ 4