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A blind deconvolution method based on L1/L2 regularization prior in the gradient space 10

A blind deconvolution method based on L1/L2 regularization p...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

作者： Cai, Ying Shi, Yu Hua, Xia School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan Hubei430074 China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617179

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results. © 2018 SPIE.

关键词： image reconstruction

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Proceedings of SPIE - The international Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

ISBN: (纸本)9781510617230

The proceedings contain 13 papers. The topics discussed include: a fast method for single image haze removal based on multiscale dark channel prior;a GPU-based mipmapping method for water surface visualization;hardware-efficient implementation of digital FIR filter using fast first-order moment algorithm;simulation and analysis of traffic flow based on cellular automation;a novel configurable VLSI architecture design of window-based image processing method;novel structures for discrete Hartley transform based on first-order moments;route constraints model based on polychromatic sets;analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem;a threshold-based fixed predictor for JPEG-LS image compression;a block-based JPEG-LS compression technique with lossless region of interest;sparse representations via learned dictionaries for x-ray angiogram image denoising;research on segmentation based on multi-atlas in brain MR image;and application of deep learning in the identification of TAO.

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Fusion of shallow and deep features for classification of high-resolution remote sensing images 10

Fusion of shallow and deep features for classification of hi...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Gao, Lang Tian, Tian Sun, Xiao Li, Hang China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds. one includes the widely used principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

关键词： image classification convolutional neural network gray-level co-occurrence matrix high-resolution remote sensing imagery

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Introduction

Proceedings of SPIE - The International Society for Optical ...

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Proceedings of SPIE - The international Society for Optical Engineering 2018年 10611卷 xiii-xiv页

作者： Bhanu, Bir

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A NDVI assisted remote sensing image adaptive scale segmentation method 10

A NDVI assisted remote sensing image adaptive scale segmenta...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Zhang, Hong Shen, Jinxiang Ma, Yanmei Yunnan Land & Resources Vocat Coll Dept Land Management Kunming 652501 Yunnan Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

关键词： Adaptive segmentation Multi-scale Remote sensing NDVI

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Research on Segmentation Based on Multi-Atlas in Brain MR image 10

Research on Segmentation Based on Multi-Atlas in Brain MR Im...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Parallel processing of images and Optimization Techniques;and Medical Imaging

作者： Qian, Yuejing Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Zhejiang Ind & Trade Vocat Coll Wenzhou 325000 Zhejiang Peoples R China

ISBN: (数字)9781510617247

ISBN: (纸本)9781510617247;9781510617230

Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

关键词： Brain segmentation Registration Atlas Abnormity detection Label fusion

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A threshold auto-adjustment algorithm of feature points extraction based on grid 10

A threshold auto-adjustment algorithm of feature points extr...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

作者： Yao, Zili Li, Jun Dong, Gaojie School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617193

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing;if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work. © 2018 SPIE.

关键词： Feature extraction

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Proceedings of SPIE - The international Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

ISBN: (纸本)9781510617193

The proceedings contain 27 papers. The topics discussed include: joint image restoration and location in visual navigation system;target tracking system based on preliminary and precise two-stage compound cameras;robust object tracking based on self-adaptive search area;saliency detection algorithm based on LSC-RC;discussion among different methods of updating model filter in object tracking;a street rubbish detection algorithm based on sift and RCNN;complex background suppression using global-local registration strategy for the detection of small-moving target on moving platform;a new method of small target detection based on neural network;a novel line segment detection algorithm based on graph search;and infrared images target detection based on background modeling in the discrete cosine domain.

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A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image 10

A sea-land segmentation algorithm based on multi-feature fus...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Li, Jing Xie, Weixin Pei, Jihong ShenZhen Univ ATR Key Lab Natl Def Technol Shenzhen 518060 Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the Multi Gaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

关键词： remote sensing image sea-land segmentation multi-feature fusion multi-gaussian background model

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Bathymetric mapping of shallow water surrounding Dongsha Island using QuickBird image 10

Bathymetric mapping of shallow water surrounding Dongsha Isl...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Li, Dongling Zhang, Huaguo Lou, Xiulin Second Inst Oceanog State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Zhejiang Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

This article presents an experiment of water depth inversion using the band ratio method in Dongsha Island shallow water. The remote sensing data is from QuickBird satellite on April 19, 2004. The bathymetry result shows an extensive agreement with the charted depths. 129 points from the chart depth data were chosen to evaluate the accuracy of the inversion depth. The results show that when the water depth is less than 20m, the inversion depth is accord with the chart, while the water depth is more than 20m, the inversion depth is still among 1525m. Therefore, the remote sensing methods can only be effective with the inversion of 20m in Dongsha Island shallow water, rather than in deep water area. The total of 109 depth points less than 20m were used to evaluate the accuracy, the root mean square error is 2.2m.

关键词： Bathymetric mapping remote sensing shallow water Dongsha Island

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