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Spectral group attention networks for hyperspectral image classification 11

Spectral group attention networks for hyperspectral image cl...

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Li, Zhengtao Wang, Zhongyang Xu, Hai Zhang, Yaozong Zhang, Tianxu Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636347

Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, most of the classification methods have the problem of insufficient expression ability to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group CNN features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the "feature recalibration" strategy is used to recalibrate the spectral group CNN features. The experiment show that the proposed approach can improve the classification accuracy of categories with subtle differences.

关键词： Hyperspectral classification attention mechanism spectral group feature

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Method of quality assessment based on convolution feature similarity for laser disturbing image 11

Method of quality assessment based on convolution feature si...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Gao, Xiang Hu, Jing Ren, LiJun Zheng, WeiPing Li, XiangJun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Yanan Univ Sch Phys & Elect Yanan 716000 Peoples R China

ISBN: (纸本)9781510636385

At present, most of the full reference laser disturbing image quality assessment methods need to know the position information of the disturbing spot and the target in advance, so that the assessment process is restricted by the prior knowledge and the preprocessing method. Aiming at this problem, this paper proposes a laser disturbing image quality assessment method based on convolution feature similarity (CNNSIM), which analyzes the output features of the image before and after laser disturbing in the convolution network. The occlusion degree of key information in the disturbing image is assessed by using the hierarchy and the sensitivity to occlusion of features, thus avoiding the input requirement of target/spot location information. The simulation experiment verifies the effectiveness of the new assessment method in different scenarios.

关键词： image quality assessment Laser disturbing assessment Convolution network

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Hybrid Feature Fusion for Person recognition in Photo Albums 11

Hybrid Feature Fusion for Person Recognition in Photo Albums

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Li, Sheng Huang, Likun Zhang, Wei Tang, Bing Wuhan Inst Technol Sch Elect & Informat Engn Hubei Engn Res Ctr Video Image & HD Project Wuhan 430205 Peoples R China

ISBN: (纸本)9781510636385

The current work on person recognition in photo albums mainly utilize pure deep convolutional features to describe a person's image. However, we observe that the hand-crafted features are usually able to provide complementary information and are more stable for identity recognition under some challenging circumstances. In view of this, we propose a novel hybrid method for person recognition in photo albums. In the proposed method, both the hand-crafted features and deep convolutional features are extracted from every person's image. These multi-modality features are then fused by a weighted average method and classified by a pre-trained SVM in the recognition procedure. The experimental results demonstrates the effectiveness of the proposed method.

关键词： Person recognition CNNs feature Hand-crafted features Feature fusion

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Single image Super-Resolution Based on Enhanced Deep Residual GAN 11

Single Image Super-Resolution Based on Enhanced Deep Residua...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Chen, Zhiyong Hu, Jing Zhang, Xuyang Li, Xiangjun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Yanan Univ Sch Phys & Elect Yanan 716000 Peoples R China

ISBN: (纸本)9781510636385

With the application of image more and more widely, People put forward higher requirements on the image quality of small objects and details in the image. In recent years, with the development of deep learning, it achieved good results in the research of image super-resolution. In this paper, we proposed EDSRGAN, a single image super-resolution(SISR) algorithm, based on enhanced residual network and the adversarial network. Compared with SRGAN, which is also based on the adversarial network, EDSRGAN can greatly reduce the high-frequency noise contained in the super-resolution(SR) image, and it also leads SRGAN in terms of peak signal to noise ratio and structural similarity evaluation indicators. Although EDSRGAN lagged behind EDSR in terms of peak signal to noise ratio and structural similarity, the SR images generated by EDSRGAN were sharper than EDSR in the object edges and targets details. EDSRGAN could achieve good results in image super-resolution on small targets.

关键词： single image super-resolution generative adversarial networks residual network convolutional neural networks

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Blind Deblurring of Gaussian Blurred images by Blurred Edge image 11

Blind Deblurring of Gaussian Blurred Images by Blurred Edge ...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Li, Yacheng Li, Lerenhan Li, Wenhao Fan, Zhihua Chen, Zongmei Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Shanghai Aerosp Control Technol Inst Infrared Detect Technol Res & Dev Ctr CASC Shanghai 201109 Peoples R China

ISBN: (纸本)9781510636385

In this paper, we propose a learning method for deblurring Gaussian blurred images blindly by exploiting edge cues via deep multi-scales generative adversarial network: DeepEdgeGAN. We proposed the edges of the blurred images to be incorporated with the blurred image as the input of the DeepEdgeGAN to provide a strong prior constraint for the restoration, which is beneficial to solve the problem that gradients of the restored images with GANs methods tend to be smooth and not clear enough. Further, we introduce the perceptual and edge as well as scale losses to train the DeepEdgeGAN. With the trained end-to-end model, we directly restore the latent sharp images from blurred images and avoiding the estimation of pixel-kernel. Qualitative and quantitative experiments demonstrate that the visual effect of the restored images significantly improves better.

关键词： Gaussian Blurred Edge of the Blurred image Deep Multi-scales Generative Adversarial Network End-to-end

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Dehazing Network based on Haze Density 11

Dehazing Network based on Haze Density

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Hua, Yanling Zuo, Zhengrong Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

ISBN: (纸本)9781510636385

Single image dehazing is a challenging ill-posed restoration problem. Most of dehazing algorithms follow the classical atmospheric scattering model and adopt same parameters for different hazy density areas in hazy images. In this paper, we proposed an end-to-end dehazing algorithm, called Dehazing Network based on Haze Density(DNBHD). The proposed network involves a haze density map estimation network and a dehazing network. By the estimated haze density map, hazy image is divided into a mist region and a dense fog region which are respectively feed into dehazing network. Compared with previous dehazing algorithm, DNBHD is independent on the atmospheric scattering model, and considers uniform fog distribution in images. We use different parameters to handle different hazy density regions, avoiding color distortion and inappropriate brightness caused by overall defogging. The experiments show our algorithm achieves significant improvements over the state-of-the-art methods.

关键词： Single image dehazing Haze density Transmission map estimation End-to-end defogging

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image caption generation method based on adaptive attention mechanism 11

Image caption generation method based on adaptive attention ...

引用

11th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Jin, Huazhong Wu, Yu Wan, Fang Hu, Man Li, Qingqing Hubei Univ Technol Sch Comp Wuhan Hubei Peoples R China

ISBN: (纸本)9781510636385

An image caption generation model with adaptive attention mechanism is proposed for dealing with the weakness of the image description model by the local image features. Under the framework of encoder and decoder architecture, the local and global features of images are extracted by using inception V3 and VGG19 network models at the encoder. Since the adaptive attention mechanism proposed in this paper can automatically identify and acquire the importance of local and global image information, the decoder can generate sentences describing the image more intuitively and accurately. The proposed model is trained and tested on Microsoft COCO dataset. The experimental results show that the proposed method can extract more abundant and complete information from the image and generate more accurate sentences, compared with the image caption model based on local features.

关键词： image caption adaptive attention image feature deep neural network

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SRNet: A Cascade Network to Speckle Reduction of SAR image 11

SRNet: A Cascade Network to Speckle Reduction of SAR Image

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Shen, Konghuai Wang, Xinglong Huang, Long Hu, Zhe Yang, Weidong Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Hubei Aerosp Technol Acad Syst Design Inst Wuhan 430040 Peoples R China

ISBN: (纸本)9781510636347

Speckle noise limits the usage of synthetic aperture radar (SAR) for object recognition and segmentation tasks. Most traditional methods sacrifice useful image information to achieve speckle reduction. The classic method based on local sliding window filtering has obvious side effect of erasing object edges and blurring texture information comparing with ground truth image. Another widely used method is convolutional neural network based on mean squared error, the visual effect of denoised image is not satisfactory even though MSE loss can have higher peak signal-to-noise ratio (PSNR) performance. In this paper, we present a cascade network to address this problem, namely SRNet, which employs an asymmetric architecture for the task of speckle noise reduction. The cascade architecture can supervise the network to revise on both pixel-wise level and feature-wise level by calculating correlation coefficient loss on the feature maps. In the meanwhile, we utilize the auxiliary loss on the intermediate results to accelerate the convergence of the network. The proposed network preserves the edge texture details much better than other compared methods.

关键词： SAR speckle noise cascade network correlation loss

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ISAR image Visualization for Aerospace Targets 11

ISAR Image Visualization for Aerospace Targets

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Liu, Yang Li, Gang SHi, Si Zhang, Chi Li, Bingning Xian Satellite Control Ctr Xian 710043 Shaanxi Peoples R China 31011 Troops PLA Beijing Peoples R China

ISBN: (纸本)9781510636347

Due to the large dynamic range and low contrast, inverse synthetic aperture radar (ISAR) image is not appropriate for human observation. In order to output and display the target imaging results, a procedure which compresses the dynamic range of the raw images into a lower range is necessary. In this paper, by analyzing the histogram of original ISAR images, the characteristics of ISAR images are investigated. Given the sparse amplitude distribution of original ISAR image and the shortcomings existing in the sparse linear histogram, this paper proposes an ISAR image detail enhancement algorithm (ISARIDE) based on histogram equalization and dynamic range compression. The advantage of the proposed method is that it can retain the target structure infounation, and improve the visual effect of human eye to target details as soon as possible. The proposed algorithm was tested on the simulated data and real data. The selected target is a flying Boeing *** results show the validity of the algorithm.

关键词： inverse synthetic aperture radar (ISAR) dynamic range compression (DRC) image detail enhancement visualization aerospace targets

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An improved contrast fusion approach in gradient domain for low light level image enhancement 11

An improved contrast fusion approach in gradient domain for ...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Yang, Shuning Song, Qiong Guo, Xin Wang, Yuehuan Sci & Technol Low Light Level Night Vis Lab Kunming Yunnan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9781510636347

images often suffer from low visibility under nonuniform illumination, weak luminance and backlight environment. This paper describes a novel approach to improvement the visualization of poor light conditions. Firstly, we raise the global brightness using an adaptive exponent induced function. To enhance the local detail perception, the local contrast is boosted by contrast preserving which utilizes human vision system model. To not bias from original image, we generate the contrast combined original image and global illuminance enhance output in the gradient domain. To reduce artifacts, the guided filter is employed to estimate the local mean illuminance when transform the contrast. The experimental results demonstrate that our proposed method has a pleasant visual effect and low computational complexity than the state of the arts.

关键词： image enhance Gradient domain Low light level

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