A New Ratio Image Based CNN Algorithm for SAR Despeckling

Vitale, Sergio; Ferraioli, Giampaolo; Pascazio, Vito

doi:10.1109/igarss.2019.8899245

Cited by 30 publications

(41 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid overfitting, speckle data are generated on the fly. A very simple denoiser is proposed in [42] with the goal to show the value of an additional loss term accounting for the Kullback-Leibler divergence between original and despeckled data, so as to ensure fidelity of first-order statistics. However, a very limited experimental validation is carried out.…”

Section: Related Workmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

View full text Add to dashboard Cite

We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.

show abstract

Section: Related Workmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning technology, especially the convolutional neural network (CNN) has emerged as a powerful tool for image construction and processing [16][17][18]. Previously, the CNN has been successfully applied to implement speckle elimination [19,20], target classification [21,22], and recognition [23] in the field of SAR imaging. Besides, CNN-based fast computed tomography (CT) image construction has also been proposed to address the sparseview problem [24].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

Sun

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

We propose and demonstrate a convolutional neural network (CNN)-based fast back projection (FBP) imaging method, which has noise-resistant capability in strong noise conditions. In this method, the desired high-resolution image is constructed from a low-resolution back projection (BP) image using a pre-trained CNN. Compared to the high-resolution imaging with basic BP algorithm, the proposed CNN-based FBP imaging has significantly reduced complexity, enabling a fast imaging speed. Meanwhile, by training the CNN using noiseless images as the desired output, the CNN-based FBP imaging is noiseresistant, which helps to obtain high-quality images in strong noise scenarios. Performance of this CNNbased FBP imaging method is investigated and compared with basic BP imaging and other methods through extensive numerical simulations. The results show that, using a CNN with optimized structure, the proposed method can greatly improve the imaging speed. Meanwhile, high-quality images with improved peak signal to noise ratios (PSNRs) are obtained in low signal-to-noise-ratio (SNR) conditions. This CNN-based FBP imaging method is expected to find applications where high-quality and fast radar imaging is required. INDEX TERMS Synthetic aperture radar, back projection algorithm, fast back projection imaging, convolutional neural network, high-resolution imaging.

show abstract

“…i.e., Kuan [3], Lee [4], and Γ-MAP [5] filters, the non-local techniques, i.e., PPB-it [6], SAR-BM3D [7], NL-SAR [8], and MuLoG [9] filtering algorithms, and frequency-based filters including the Wiener Filter (WF) [10]. Recently, deep learning-based approaches have been gaining interest in the SAR image despeckling field [11][12][13]. Concerning the spatial approaches, proceeding with these techniques does not need too much memory occupation and seems to be important in terms of fast processing time.…”

Section: Introductionmentioning

confidence: 99%

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

et al. 2019

Self Cite

View full text Add to dashboard Cite

Speckle noise is presented as an inherent dilemma that affects the image processing field, and in particular synthetic aperture radar images. In order to mitigate the adverse effects caused by this phenomenon, several approaches have been introduced in the scientific community during the last three decades including spatial-based and non-local filtering approaches. However, these proposed techniques suffer from some limitations. In fact, it is very difficult to find an approach that is able, on the one hand, to perform well in terms of noise reduction and image detail preservation and, on the other hand, provide a filtering output solution without high computational complexity and within a short processing time. In this paper, we aim to evaluate the performance of a newly-developed despeckling algorithm, presented as an enhancement of the classical Wiener filter and properly designed to work with a Graphics Processing Unit (GPU). The algorithm is tested on both a simulated framework and real Sentinel-1 SAR data. The results, obtained in comparison with other filters, are interesting and promising. Indeed, the proposed method turns out to be a useful filtering instrument in the case of large images by performing the processing within a limited time and ensuring good speckle noise reduction with a considerable image detail preservation.

show abstract

A New Ratio Image Based CNN Algorithm for SAR Despeckling

Cited by 30 publications

References 12 publications

Nonlocal CNN SAR Image Despeckling

Nonlocal CNN SAR Image Despeckling

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

Fast GPU-Based Enhanced Wiener Filter for Despeckling SAR Data

Contact Info

Product

Resources

About