SAR image despeckling using a dilated densely connected network

Gui, Yunchuan; Xue, Lei; Li, Xiuhe

doi:10.1080/2150704x.2018.1492170

Cited by 20 publications

(9 citation statements)

References 13 publications

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“…In addition, skip connections help reduce the vanishing gradient problem. Along the same line, Gui et al [37] used dilated convolution and residual learning with a densely connected network. In addition, Li et al [38] relied on dilated convolution and residual training, the main innovation being the use of a convolutional block attention module to enhance representation power and performance.…”

Section: Related Workmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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“…Even though ablation studies seem to support the importance of attention modules, in general, it is not obvious how spatial attention, in particular, helps achieving a better image restoration. SAR-DDCN, proposed in [53] is conceptually similar to SAR-DRN, the main innovation being the introduction of two 5-layer dense blocks. Dense connections [79] are well known to allow a better propagation of features in very deep networks and hence reduce the vanishing gradient problem, a major issue for the training of very deep CNNs.…”

Section: A Direct Dl-based Despecklingmentioning

confidence: 99%

“…Indeed, the explicit goal of the method, based on the analysis of previous literature, is to use a deeper architecture to extract more expressive features. The computational load is reduced by means of simplified dense connections: 5-layer blocks are used as in [53], but only the last layer in the block receives inputs from all preceding layers. Then, the same structure is replicated at the block level, thus constructing a hierarchical multiconnection network.…”

Section: A Direct Dl-based Despecklingmentioning

confidence: 99%

Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives

Fracastoro,

Magli,

Poggi

et al. 2020

Preprint

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Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called "speckle", which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This paper surveys the literature on deep learning methods applied to SAR despeckling, covering both the supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods with the objective to recognize the most promising research lines, to identify the factors that have limited the success of deep models, and to propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.

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“…Zhang [39] combined skip connection [40] and dilated convolution [41] to achieve SAR despeckling. Similarly, Gui [42] proposed a network using dilated convolution and a densely connected network [43]. Lattari [44] successfully used the U-Net CNN architecture for SAR image speckle suppression.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

et al. 2021

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Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

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SAR image despeckling using a dilated densely connected network

Cited by 20 publications

References 13 publications

Nonlocal CNN SAR Image Despeckling

Nonlocal CNN SAR Image Despeckling

Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives

Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

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