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- W4313443693 abstract "In synthetic aperture radar (SAR) images, the speckle noise is inherent which is in granular form and oftenly defined as a multiplicative nature. Due to multiplicative nature not only the qualities of the SAR images get degraded but also all coherent images. In order to deals with granular form of noise, there are various despeckling techniques has been developed from the few decades. The primary objective of this paper is to majorly focus on the existing despeckling model with its emerging trends and greatly transform in mechanism. The nature cum characteristics of the speckle noise has been discussed in detail. Furthermore, the cons of the homomorphic filtering mechanisms are highlighted. The classification of various despeckling methods based on spatial and transform domain which can play the most significant role in the designing of the robust techniques has addressed. The efficiency of the despeckling methods gets analyzed using various performance metrics such as SNR, PSNR, SSIM, and UIQI. Also, the visual appearance of the any despeckling method can be analyzed using confusion matrix and Cohen’s kappa method." @default.
- W4313443693 created "2023-01-06" @default.
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- W4313443693 date "2023-01-01" @default.
- W4313443693 modified "2023-10-17" @default.
- W4313443693 title "Deep Learning-Based Suppression of Speckle-Noise in Synthetic Aperture Radar (SAR) Images: A Comprehensive Review" @default.
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- W4313443693 doi "https://doi.org/10.1007/978-981-19-2358-6_62" @default.
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