scholarly journals Speckle reduction in SAR images based on an adaptive filtering in the frequency domain

2020 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Hamid Jannati ◽  
Mohammad Javad Valadan Zoej
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Speckle Reduction ◽  
Sar Images

scholarly journals Speckle reduction in SAR images based on an adaptive filtering in the frequency domain

2020 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Hamid Jannati ◽  
Mohammad Javad Valadan Zoej
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Speckle Reduction ◽  
Sar Images

Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images

2015 ◽  
Vol 43 (4) ◽  
pp. 739-750 ◽  
Author(s):  
Sanjay Shitole ◽  
Shaunak De ◽  
Y. S. Rao ◽  
B. Krishna Mohan ◽  
Anup Das
Keyword(s):  
Window Size ◽  
Speckle Reduction ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Selection Of

Global Image Dehazing via Frequency Perception Filtering

2019 ◽  
Vol 28 (09) ◽  
pp. 1950142
Author(s):  
Linli Xu ◽  
Jing Han ◽  
Tian Wang ◽  
Lianfa Bai
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Low Frequency ◽  
Intermediate Frequency ◽  
Human Visual Perception ◽  
Image Dehazing ◽  
Weighting Factors ◽  
Outdoor Scenes ◽  
Frequency Domain Filtering ◽  
Three Components

In outdoor scenes, haze limits the visibility of images, and degrades people’s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components — low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes.

scholarly journals SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

2020 ◽  
Vol 12 (16) ◽  
pp. 2636
Author(s):  
Emanuele Dalsasso ◽  
Xiangli Yang ◽  
Loïc Denis ◽  
Florence Tupin ◽  
Wen Yang
Keyword(s):  
Neural Networks ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Hybrid Approach ◽  
White Gaussian Noise ◽  
Training Data ◽  
Speckle Reduction ◽  
Sar Image ◽  
Sar Images

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

scholarly journals Speckle Reduction in OCT using Massively-Parallel Detection and Frequency-Domain Ranging

2006 ◽  
Vol 14 (11) ◽  
pp. 4736 ◽  
Author(s):  
A. E. Desjardins ◽  
B. J. Vakoc ◽  
G. J. Tearney ◽  
B. E. Bouma
Keyword(s):  
Frequency Domain ◽  
Massively Parallel ◽  
Speckle Reduction ◽  
Parallel Detection

Undecimated double density wavelet transform based speckle reduction in SAR images

2009 ◽  
Vol 35 (1) ◽  
pp. 209-217 ◽  
Author(s):  
D. Gnanadurai ◽  
V. Sadasivam ◽  
J. Paul Tiburtius Nishandh ◽  
L. Muthukumaran ◽  
C. Annamalai
Keyword(s):  
Wavelet Transform ◽  
Speckle Reduction ◽  
Sar Images

The multilook polarimetric whitening filter (MPWF) for intensity speckle reduction in polarimetric SAR images

1998 ◽  
Vol 36 (3) ◽  
pp. 1016-1020 ◽  
Author(s):  
Guoqing Liu ◽  
Shunji Huang ◽  
A. Torre ◽  
F. Rubertone
Keyword(s):  
Speckle Reduction ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Whitening Filter
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