Convolutional Neural Network and Guided Filtering for SAR Image Denoising

Liu, Shuaiqi; Tong, L.; Gao, Lele; Li, Hailiang; Hu, Qi; Zhao, Jie; Wang, Chong

doi:10.3390/rs11060702

Cited by 63 publications

(27 citation statements)

References 40 publications

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“…Pan et al [45] followed the same approach but replaced the DnCNN denoiser with the faster FFDNet denoiser [46], which uses combined downsampling-upsampling steps to improve efficiency. Then, in [25], homomorphic filtering is performed based on multiple instances of the same CNN [47] trained on Gaussian noise at various levels of intensity. The output images are then combined by means of guided filtering driven by an edge map.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, there is a growing interest also for deep learning-based SAR image despeckling. Methods based on convolutional neural networks (CNN) [22,23] and generative adversarial networks (GAN) [24] have been proposed already back in 2017, and new methods keep appearing at a growing rate [25,26]. Nonetheless, improvements over the previous state of the art have been quite limited to date.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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“…In recent years, with the significant improvement in computing power, convolutional neural networks (CNN) have been widely used in image fusion, image segmentation, image classification, image recognition, image denoising and other fields. CNNs demonstrate their powerful automatic feature learning capability by designing a multi-layer network structure [21]- [25]. As an end-to-end model, CNN can deeply learn its features of multiple levels by setting with different levels.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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“…Meanwhile, it seems that making the DL model possess the self-correcting but not the invariant denoising capability is the key to achieving superiority in SAR image denoising. Inspired by the denoising evaluation index proposed in [38] and [35], we introduce the concept of the texture level map (TLM) and design a two-component DL network to address the above issue. The TLM, obtained by one index that is initially used as the statistical measure of the quality of the noise ratio image [38], is a heatmap that shows the randomness, homogeneity and scale of the pattern distribution of an image.…”

Section: Introductionmentioning

confidence: 99%

A Two-Component Deep Learning Network for SAR Image Denoising

Zhang

Wang

2020

IEEE Access

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Speckle noise reduction and detail preservation, between which a balance is hard to achieve, are two main purposes of synthetic aperture radar (SAR) image denoising. For different regional characteristics of SAR images, satisfactory denoising results are obtained by complex parameter fine-tuning in most methods, and a solution with strong robustness seems difficult to find. In this paper, a novel twocomponent deep learning (DL) network is proposed to solve the above problem. First, the texture estimation subnetwork is constructed to produce the texture level map (TLM), which evaluates the randomness and scale of the texture distribution. Then, the noise removal subnetwork learns a spatially variable mapping between the noise and clean images with the help of TLM. Once the network has been trained, it can automatically quantify the texture feature and decide whether to smooth the local noise or maintain the detail. Comprehensive experiments on simulated and real SAR images demonstrate the superior performance of the proposed method over the state-of-the-art methods with respect to both the visual effect and quantitative analysis. INDEX TERMS SAR image despeckling, deep learning, texture level map.

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Convolutional Neural Network and Guided Filtering for SAR Image Denoising

Cited by 63 publications

References 40 publications

Nonlocal CNN SAR Image Despeckling

Nonlocal CNN SAR Image Despeckling

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

A Two-Component Deep Learning Network for SAR Image Denoising

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