SAR Image Specle Reduction based on a Generative Adversarial Network
| Autor: | Yangyang Li, Licheng Jiao, Ruijiao Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
Ground truth Computer science Speckle reduction business.industry Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Speckle noise Image processing 02 engineering and technology Reduction (complexity) Speckle pattern Transformation (function) Distortion 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 021101 geological & geomatics engineering |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9206847 |
| Popis: | Synthetic aperture radar (SAR) image despeckling is recognized as the basis for SAR image processing and interpretation. Over the past decades, many impressed speckle reduction methods have been developed and achieved good performance under certain circumstances. However, how to suppress speckle noise in a homogeneous region while more effectively protecting details and avoid distortion of data features caused by homomorphic transformation is still an urgent problem. In this paper, a novel speckle reduction algorithm based on generative adversarial network (GAN) is proposed, which contains a generator and a discriminator. For the generator that is used directly for subsequent noise reduction, a total variation (TV) loss function is added. Meanwhile, we directly learn the mapping between the input image and the ground truth rather than the logarithmic transformation. Indeed, the improved lightweight discriminative network will also provide learning guidance for the generator. Experiments on simulatedSAR images and real SAR images demonstrate the improvement in visual and statistical performance comparing to the state-of-the-art despeckling algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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