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	1. A hybrid model for seam-carving and JPEG compression operation chain detection
	BIN Muyu,YANG Gaobo,DONG Xiaoxu
	Electrics, Communication and Autocontrol Technology 22 April 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (179K B)

	Abstract:Seam carving, which is known as content-aware image resizing, leaves no visible traces in resized images. Though several approaches have been presented for seam carving forgery detection, there are few works about the operation chain detection of both seam carving and JPEG compression. In this paper, a hybrid model is proposed to distinguish JPEG-Seam carving-JPEG images from single and double compression JPEG images. Both spatial-domain and transform-domain features are extracted for operation chain detection. Experiment results show that the proposed approach can efficiently detect seam carving operation.
	TO cite this article:BIN Muyu,YANG Gaobo,DONG Xiaoxu. A hybrid model for seam-carving and JPEG compression operation chain detection[OL].[22 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748494


	2. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks
	Liu Liwei,Peng Haipeng,Li Lixiang,Yang Yixian
	Electrics, Communication and Autocontrol Technology 11 January 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (1674K B)

	Abstract:With the rapid development of the applications of wireless sensor networks (WSNs) in various fields, such as internet of things, military collaborative operations, e-government, telemedicine, etc., the security, the energy-efficiency and the storage-saving are undoubtedly highlighted in the research of WSNs. Compressive sensing (CS) can compress and reconstruct sparse or compressible signals with fewer samples than those of Nyquist-Shannon theorem requires. In order to meet the requirements of storage, energy-efficiency and security of WSNs simultaneously, we propose an efficient and secure transmission model based on compressive sensing and hash-chain theory, which is called hash-chain compressive sensing (HCCS). Compared with the traditional compressive sensing, only the initial key and the hash function are used in the sensor node to decrease the storage space. And the characteristics of hash-chain assure the security of data transmission under HCCS. Furthermore, we propose an image encryption method based on HCCS in order to improve the efficiency and security of image transmission. The security of image signal is greatly improved by adopting the double-encryption mechanism, which uses the measurement matrix $\Phi _1$ and the encryption matrix $\Phi _2$. The numerical experiments are performed to show the feasibility of HCCS and the effectiveness of the proposed image transmission model.
	TO cite this article:Liu Liwei,Peng Haipeng,Li Lixiang, et al. Hash-chain Compressive Sensing for Secure and Efficient Transmission in Wireless Sensor Networks[OL].[11 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747004


	3. SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach
	Biao Hou,GuiLin Ju,HongXiao Feng,Zhichao Liu
	Electrics, Communication and Autocontrol Technology 02 May 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:A new neighborhood-adaptive non-local (NL) despeckling filter is proposed in this paper. An adaptive and point-wise fashion neighborhood that limits the bound of weighted pixels is designed, which is determined by an adaptive directional scales set and a new automatic similarity threshold. The set of adaptive directional scales constructs a rectangular neighborhood and the optimal scale is obtained with the proposed similarity threshold. The presented similarity is based on the probabilistic patch based similarity (PPB-similarity) measurement and deduced with a statistical Monte Carlo method. Experiment results show that our method can not only provide superior speckle removal when compared to probabilistic patch based non-local (PPB-NL) filter with fixed neighborhood, especially for its non-iterative version, but also show good performance in preserving details and texture information.
	TO cite this article:Biao Hou,GuiLin Ju,HongXiao Feng, et al. SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach[OL].[ 2 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4731588


	4. Super-Resolution ISAR Imaging via Cosparse Model
	HOU Biao,LI Zhengwei,ZHANG Guang,JIAO Licheng
	Electrics, Communication and Autocontrol Technology 02 May 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (4K B)

	Abstract:A super-resolution inverse synthetic aperture radar (ISAR) imaging based on cosparse is proposed in this paper. Different from traditional imaging model, we regard the super-resolution imaging process as an analysis model. In order to obtain well-focused and denoised ISAR image, the phase adjustment is realized by analysis operator learning (AOL), and we add a new regularization item and use Augmented Lagrangian (AL) method to approximate the denoised signal. Then we use a modified OMP algorithm to recover the strong scattering coefficients, which can produce a well-focused image. This process can be seen as a multilayer imaging model and the quality of the imaging can be improved step by step. The experimental results show that the proposed method can get higher quality ISAR image than the traditional super-resolution imaging algorithms and is an effective approach to ISAR imaging within a short CPI.
	TO cite this article:HOU Biao,LI Zhengwei,ZHANG Guang, et al. Super-Resolution ISAR Imaging via Cosparse Model[OL].[ 2 May 2017]

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