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A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution

Journal of Donghua University(English Edition) 2011年第3期28卷 305-311页

作者：陈文方向忠 Shanghai Key Laboratory of Digital Media Processing and Transmissions Institute of Image Communication and Information Processing Shanghai Jiaotong University

A novel Bayesian super resolution (SR) algorithm based on the distribution of synthetic gradient is proposed. The synthetic gradient combines prior information in horizontal, vertical, and diagonal directions. Its distribution is modeled as a Lorentzian function and regarded as a new image model which can sufficiently regularize the ill-posed algorithm and preserve the edges in the reconstructed images. The graduated nonconvexity (GNC) optimization is employed to guarantee the convergence of the proposed Lorentzian SR (LSR) algorithm to the global minimum. The performance of LSR is compared with conventional algorithms, and experimental results demonstrate that the proposed algorithm obtains both subjective and objective gains.

关键词： synthetic gradients Lorentzian distribution threshold edge preservation

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Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

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Optoelectronics Letters 2018年第6期14卷 470-475页

作者： YANG Mao-xiang TANG Gui-jin LIU Xiao-hua WANG Li-qian CUI Zi-guan LUO Su-huai Jiangsu Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications School of Electrical Engineering and Computing University of NewcastleNSW 2308 Australia

In order to enhance the contrast of low-light images and reduce noise in them, we propose an image enhancement method based on Retinex theory and dual-tree complex wavelet transform(DT-CWT). The method first converts an image from the RGB color space to the HSV color space and decomposes the V-channel by dual-tree complex wavelet transform. Next, an improved local adaptive tone mapping method is applied to process the low frequency components of the image, and a soft threshold denoising algorithm is used to denoise the high frequency components of the image. Then, the V-channel is rebuilt and the contrast is adjusted using white balance method. Finally, the processed image is converted back into the RGB color space as the enhanced result. Experimental results show that the proposed method can effectively improve the performance in terms of contrast enhancement, noise reduction and color reproduction.

关键词： Retinex theory dual-tree complex wavelet transform image enhancement

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Improved SAI method using non-local spatial constraint for image interpolation

Improved SAI method using non-local spatial constraint for i...

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2011 International Conference on Wireless communications and Signal processing, WCSP 2011

作者： Bian, Jueping Gan, Zongliang Zhang, Mengcheng Zhu, Xiuchang Jiangsu Province Key Lab on Image Processing and Image Communication NUPT Nanjing China

ISBN: (纸本)9781457710100

This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which is called non-local property. We can use this non-local strategy to improve the interpolation quality by better estimating the model parameters and Lagrangian multiplier. There are two steps in our method. In the first step, similar patches of the given block are found in the initialized high resolution image, and the model parameters can be determined properly using the expanded piecewise auto regression (PAR) model and non-local spatial constraint. In the second step, the self-similarity of patches across the high and low resolution images is exploited to solve the Lagrangian multiplier , thus to make the data estimation robust. Experiments indicate that the improved method can achieve good results both subjectively and objectively. © 2011 IEEE.

关键词： Interpolation

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Distributed Adaptive Compressed Video Sensing Using Smoothed Projected Landweber Reconstruction

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China communications 2013年第11期10卷 58-69页

作者：李然干宗良崔子冠武明虎朱秀昌 Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and Telecommunications

A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this *** this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS *** key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis *** the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s *** fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge ***,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the *** results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.

关键词： distributed video coding com-pressed sensing side information smoothedprojected Landweber reconstruction edge inf-ormation

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Action Recognition from a Different View

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China communications 2013年第12期10卷 139-148页

作者：陈昌红干宗良 Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and Telecommunications

In this paper,we propose a novel approach to recognise human activities from a different *** appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,there must be some regularity among the same action sequences of different *** matrices appear to be relative stable across ***,the ability to effectively realise this stability is a *** this paper,we extract the shape-flow descriptor as the low-level feature and then choose the same number of key frames from the action ***-similarity matrices are obtained by computing the similarity between any pair of the key *** diagonal features of the similarity matrices are extracted as the highlevel feature representation of the action sequence and Support Vector Machines(SVM) is employed for *** test our approach on the IXMAS multi-view data *** proposed approach is simple but effective when compared with other algorithms.

关键词： action recognition different view shape-flow descriptor self-similarity matrix diagonal feature

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Compressed Video Super-resolution Reconstruction Based on Regularized Algorithm

Compressed Video Super-resolution Reconstruction Based on Re...

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2006 8th International Conference on Signal processing

作者： Xu Zhong-qiang Gan Zongliang Zhu Xiu-chang Information Industry Ministry and Jiangsu Province Key Lab of Image Processing & Image Communication Nanjing University of Posts & Telecommunications

ISBN: (纸本)0780397371

Estimating high-resolution(HR) video from a sequence of low-resolution(LR) compressed observations is the focus of this *** on the theory of regularization,this paper proposes a new form of regularized cost function to control the within-channel balance between received data and prior information,and a channel weight coefficient to control the cross-channel *** LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated for each channel with ameliorating artifacts in compressed *** iterative gradient descent algorithm is utilized to reconstruction the HR *** results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality.

关键词： Regularization multichannel super-resolution reconstruction compressed video

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： image fusion

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Cone-Beam Computed Tomography Limited-Angle image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing 210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing 210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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Stereo Matching based on Robust Likelihoods and MST Leveraged Smoothness Priors

Stereo Matching based on Robust Likelihoods and MST Leverage...

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Tianliang Liu Liang Wang Xiuchang Zhu Jiangsu Provincial Key Lab of Image Processing and Image Communication College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467321969

This paper proposes a global stereo correspondence using robust matching likelihoods and minimum spanning tree （MST） leveraged smooth priors in a probabilistic graphical model framework. The matching likelihoods of the stereo correspondence can be robustly constructed as data term by aggregating initial matching costs from Weber local descriptors using an unsymmetrical guided filtering in a linear model. The disparity priors are devised as smooth term to characterize the smoothness constraints leveraged by the MST structure. The presented stereo approach provides an effective and efficient way to reflect robust visual dissimilarity and resolve local and regional discontinuities. Experiments demonstrate that the proposed global stereo matching method can produce piecewise smooth, accurate and dense disparity map, while removing effectively the visual ambiguity of the stereo matching problem.

关键词： stereo vision Markov random field Weber descriptor minimun spanning tree QPBO optimization

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Flicker-Free Adaptive Enhancement for Infrared Video 13

Flicker-Free Adaptive Enhancement for Infrared Video

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13th International Conference on Wireless communications and Signal processing, WCSP 2021

作者： Wei, Caixia Gan, Zongliang Nanjing University of Posts and Telecommunications Jiangsu Provincial Key Lab of Image Processing and Image Communication Nanjing China

ISBN: (纸本)9781665407854

Infrared videos often suffer from low contrast and insufficient texture. In order to improve poor visual effect, a novel infrared video enhancement method is proposed, which contains intra-frame brightness enhancement and inter-frame brightness continuity adjustment. In intra-frame brightness enhancement, it has two steps including global contrast enhancement and local details enhancement. To global contrast enhancement, the processed image contrast by geometric distribution, and it is incorporated into an optimization problem with a sorted histogram of each frame to yield a modified histogram. To local details enhancement, after Retinex decomposition, the reflection component is magnified by boundary preserving filter, and this strategy can effectively suppress noise in relatively uniform regions. Furthermore, to maintain inter-frame brightness continuity, adaptive parameters are introduced through optical flow to control the combination ratio of geometric distribution histogram, in which the enhancement degree of each frame can be adjusted according to inter-frame similarity and the moving region are enhanced adaptively according to the change of each frame. Experimental results demonstrate that proposed algorithm can effectively enhance the contrast and detail of infrared video, and improve the visual effect in subjective and objective quality evaluation. © 2021 IEEE.

关键词： Luminance

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