Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

Li, Chongyi; Cong, Runmin; Hou, Junhui; Zhang, Sanyi; Ye, Qian; Kwong, Sam

doi:10.1109/tgrs.2019.2925070

Cited by 232 publications

(152 citation statements)

References 56 publications

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“…The superior performance of deep learning in image segmentation [46], image defogging [47], super resolution [48], and salient objection detection [49] had demonstrated in recent years. Moreover, deep learning-based methods were gradually applied to low-level vision problems [50], [51].…”

Section: Data-driven Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multiscale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods. INDEX TERMS Underwater image enhancement, Pixel intensity center regionalization, Histogram equalization, Multi-scale fusion.

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Section: Data-driven Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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“…Recently, with the advanced development of deep learning in computer vision tasks, such as underwater image processing [30]- [32], object salient detection [33], and image dehazing [34]. Learning-based methods have been employed in the image deblurring.…”

Section: B Learning-based Methodsmentioning

confidence: 99%

Attention Network for Non-Uniform Deblurring

Guo

Jin

2020

IEEE Access

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Recently, image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods can not produce satisfactory results, such as lacking of salient structures and fine details. In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. In this paper, we investigate an attention network for image deblurring. Instead of relying on local receptive fields to construct features by previous state-of-the-art methods, the non-local features for capturing long-range dependencies and the local features rely on receptive fields should be jointly considered. Therefore, we propose a novel dense feature fusion block that consists of a channel attention module and a pixel attention module. In addition, we further densely connected multiple dense feature fusion blocks to acquire high-order feature representation. Moreover, a scale attention module is further introduced for removing unfavorable features while retaining features that facilitate image recovery. Comprehensive experimental results show that our method is able to generate photo-realistic sharp images from real-world blurring images and outperforms state-of-the-art methods. INDEX TERMS Generative adversarial networks, non-uniform image deblurring, attention network.

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“…The subgenerator F1 was designed by adding a convolutional layer (Cov)-Bayes network (BN)-leaky rectified linear unit (ReLU) residual block [46] to U-Net [47]. The U-Net helps to perverse the features of the original image in the generated image, and the skip connection [48] between the encoder and decoder can transmit the textural features of the original images between high-level feature layers. The structure of subgenerator F1 is explained in Table I.…”

Section: Figure 2 the Structure Of Generator G1mentioning

confidence: 99%

A Novel Pedestrian Reidentification Method Based on a Multiview Generative Adversarial Network

Guo

Pang

et al. 2020

IEEE Access

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Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks. INDEX TERMS deep learning (DL), generative adversarial networks (GANs), image generation, pedestrian reidentification (PRI), classification recognition network (CRN)

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Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

Cited by 232 publications

References 56 publications

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Attention Network for Non-Uniform Deblurring

A Novel Pedestrian Reidentification Method Based on a Multiview Generative Adversarial Network

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