Weak target detection in SAR images via improved itti visual saliency model

2017 ◽  
Author(s):  
Donghui Lai ◽  
Boli Xiong ◽  
Gangyao Kuang
Keyword(s):  
Target Detection ◽  
Visual Saliency ◽  
Sar Images ◽  
Saliency Model ◽  
Visual Saliency Model ◽  
Weak Target

scholarly journals Synthetic Aperture Radar Processing Approach for Simultaneous Target Detection and Image Formation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3377 ◽  
Author(s):  
Jifang Pei ◽  
Yulin Huang ◽  
Weibo Huo ◽  
Yuxuan Miao ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Time Domain ◽  
Image Formation ◽  
Visual Saliency ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Saliency Maps ◽  
Sar Imaging ◽  
Sar Imagery

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation.

scholarly journals Visual Saliency Model-Based Image Watermarking with Laplacian Distribution

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 239
Author(s):  
Hongmei Liu ◽  
Jinhua Liu ◽  
Mingfeng Zhao
Keyword(s):  
Simulation Analysis ◽  
Image Watermarking ◽  
Additive White Gaussian Noise ◽  
Visual Saliency ◽  
High Energy ◽  
Distribution Model ◽  
Saliency Model ◽  
Detection Approach ◽  
Laplacian Distribution ◽  
Visual Saliency Model

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks.

A Novel Biologically Inspired Visual Saliency Model

2014 ◽  
Vol 6 (4) ◽  
pp. 841-848 ◽  
Author(s):  
Jingjing Zhao ◽  
Shujin Sun ◽  
Xingtong Liu ◽  
Jixiang Sun ◽  
Afeng Yang
Keyword(s):  
Visual Saliency ◽  
Biologically Inspired ◽  
Saliency Model ◽  
Visual Saliency Model

A Two-Stage Clustering Based 3D Visual Saliency Model for Dynamic Scenarios

2019 ◽  
Vol 21 (4) ◽  
pp. 809-820 ◽  
Author(s):  
You Yang ◽  
Bei Li ◽  
Pian Li ◽  
Qiong Liu
Keyword(s):  
Visual Saliency ◽  
Two Stage ◽  
Saliency Model ◽  
Visual Saliency Model

Local and Global Contrast for Saliency Estimate in DCT Domain

2013 ◽  
Vol 456 ◽  
pp. 611-615
Author(s):  
Nan Ping Ling ◽  
Han Ling Zhang
Keyword(s):  
State Of The Art ◽  
Visual Saliency ◽  
Local Contrast ◽  
Global Contrast ◽  
Contrast Method ◽  
Saliency Model ◽  
Dct Domain ◽  
Proposed Model ◽  
Roc Area ◽  
Visual Saliency Model

In this paper, we present a new bottom-up visual saliency model, which utilizes local and global contrast method to calculate the saliency in DCT domain. Our proposed method is firstly used in the DCT domain. The local contrast method uses the center-surround operation to compute the local saliency, and the global contrast method calculate the dissimilarity between DCT blocks of image and any other DCT blocks in any location. The final saliency is generated by combining the local with global contrast saliency. Experimental evaluation on a publicly available benchmark dataset shows the proposed model can acquire state-of-the-art results and outperform the other models in terms of the ROC area.

A Saliency Detection Method Based on Wavelet Transform and Simple Priors

2014 ◽  
Vol 602-605 ◽  
pp. 2238-2241
Author(s):  
Jian Kun Chen ◽  
Zhi Wei Kang
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Feature Maps ◽  
Multi Scale ◽  
Saliency Model ◽  
Proposed Model ◽  
Visual Saliency Model ◽  
Better Than

In this paper, we present a new visual saliency model, which based on Wavelet Transform and simple Priors. Firstly, we create multi-scale feature maps to represent different features from edge to texture in wavelet transform. Then we modulate local saliency at a location and its global saliency, combine the local saliency and global saliency to generate a new saliency .Finally, the final saliency is generated by combining the new saliency and two simple priors (color prior an location prior). Experimental evaluation shows the proposed model can achieve state-of-the-art results and better than the other models on a public available benchmark dataset.

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