SAR Image Despeckling Using Continuous Attention Module

Ko, Jaekyun; Lee, Sanghwan

doi:10.1109/jstars.2021.3132027

Cited by 32 publications

(13 citation statements)

References 57 publications

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“…Therefore, those datasets were also included in the learning process. The proposed SDRCNN and ABCNN methods were compared to the SARBM3D [8], DCNN [20], overcomplete convolutional neural network (OCNN) [35], and SAR image despeckling using a continuous attention module (SAR-CAM) [37] methods.…”

Section: Resultsmentioning

confidence: 99%

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Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

Pongrac

Gleich

2023

Remote Sensing

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The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.

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Section: Resultsmentioning

confidence: 99%

“…The network is trained end-to-end with synthetically generated speckled images using a composite loss function. The SAR-CAM method [37] improves the performance of the encoder-decoder CNN architecture by using various attention modules to capture multiscale information.…”

Section: Introductionmentioning

confidence: 99%

Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

Pongrac

Gleich

2023

Remote Sensing

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“…Due to the unavailability of original images for comparison, metrics such as the PSNR and SSIM were no longer applicable. To overcome this limitation, the ENL was introduced ( 30 , 31 ). The ENL serves as an indicator for evaluating the smoothness of the images in homogeneous areas, providing a metric for assessing denoising efficiency in the absence of original images.…”

Section: Methodsmentioning

confidence: 99%

Ultrasound image denoising autoencoder model based on lightweight attention mechanism

Shi,

Di,

Liu

2024

Quant Imaging Med Surg

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Background The presence of noise in medical ultrasound images significantly degrades image quality and affects the accuracy of disease diagnosis. The convolutional neural network–denoising autoencoder (CNN-DAE) model extracts feature information by stacking regularly sized kernels. This results in the loss of texture detail, the over-smoothing of the image, and a lack of generalizability for speckle noise. Methods A lightweight attention denoise-convolutional neural network (LAD-CNN) is proposed in the present study. Two different lightweight attention blocks (i.e., the lightweight channel attention (LCA) block and the lightweight large-kernel attention (LLA) block are concatenated into the downsampling stage and the upsampling stage, respectively. A skip connection is included before the upsampling layer to alleviate the problem of gradient vanishing during backpropagation. The effectiveness of our model was evaluated using both subjective visual effects and objective evaluation metrics. Results With the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values at all noise levels, the proposed model outperformed the other models. In the test of brachial plexus ultrasound images, the average PSNR of our model was 0.15 higher at low noise levels and 0.33 higher at high noise levels than the suboptimal model. In the test of fetal ultrasound images, the average PSNR of our model was 0.23 higher at low noise levels and 0.20 higher at high noise levels than the suboptimal model. The statistical analysis showed that the p values were less than 0.05, which indicated a statistically significant difference between our model and the other models. Conclusions The results of this study suggest that the proposed LAD-CNN model is more efficient in denoising and preserving image details than both conventional denoising algorithms and existing deep-learning algorithms.

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“…It can selectively emphasize effective features and suppress irrelevant ones. In recent years, researchers have widely used the attention mechanism in the field of SAR image despeckling [31,45] and achieved significant results. The channel attention mechanism used in this paper is illustrated in Figure 5.…”

Section: Network Subblock Structurementioning

confidence: 99%

Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

Zhao,

Ren,

Sun

et al. 2024

Electronics

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Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures.

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SAR Image Despeckling Using Continuous Attention Module

Cited by 32 publications

References 57 publications

Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

Ultrasound image denoising autoencoder model based on lightweight attention mechanism

Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

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