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SAR Image Despecklingthrough ConvolutionalNeural Networks

树图思维导图提供 SAR Image Despecklingthrough ConvolutionalNeural Networks 在线思维导图免费制作,点击“编辑”按钮,可对 SAR Image Despecklingthrough ConvolutionalNeural Networks  进行在线思维导图编辑,本思维导图属于思维导图模板主题,文件编号是:5d963d4883fae9d790291a3e53b2fe6b

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SAR Image Despeckling through Convolutional Neural Networks思维导图模板大纲

Ⅰ Introduction

Background

SAR images are often hindered by speckle noise, which can negatively impact automatic image analysis.

SAR image despeckling methods have become increasingly important to preserve relevant image features.

Research gap

Most speckle suppression methods rely on detailed statistical models.

The statistics can vary significantly.

Research aim

Investigate the use of discriminative model learning through CNNs for SAR image despeckling.

Synopsis of the research

Use CNNs to overcome the modeling issue for AWGN image denoising.

Ⅱ Proposed Method

Materials

SAR imgaes

Procedure (Figure 1)

Divide images into noisy and clean patches.

Subtract the clean patches from the noisy patches to get the residual patches.

Use CNN to learn residual patches by adjusting weights through loss function.

Architecture of CNN (Figure 2)

The network has 17 full convolutional layers with 64 feature maps, recovers the speckle component instead of the clean image, and uses it to subtract from the noisy image.

Ⅲ Experimental Results

Experimental Settings

The experiments were carried out in Matlab comparing results with three despeckling algorithms, PPB,SAR-BM3D,and NL-SAR by perfor-mance indexes (PSNR&SSIM&ENL).

Outcomes

CNNs creat better visual inspection with an impressive improvement in detail preservation on simulated SAR images.

CNNs achieve the best scores on real SAR image, indicating a better speckle suppression and detail preservation.

Ⅳ Conclusion

CNNs are more suitable for SAR image despeckling with the residual learning strategy compared with traditional methods.

Note

Abbr : SAR : Synthetic Aperture Radar ; AWGN : Additive White Gaussian Noise ; CNNs : Convolutional Neural Networks ; PSNR : Peak Signal-to-Noise Ratio ; SSIM : Structural Similarity ; ENL : Equivalent Number of Looks

Figure 1

Figure 2

The first paper investigating CNNs for SAR image despeckling.思维导图模板大纲

Therefore思维导图模板大纲

However思维导图模板大纲

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