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Semi-Supervised MVCT Enhancement Using Diffusion Model Refined With KVCT Priors

IEEE Transactions on Radiation and Plasma Medical Sciences 2025年第5期9卷 667-679页

作者： Mengxun Zheng Long Tang Peiwen Liang Shuang Jin Xiaotong Xu Zhe Su Hua Zhang School of Biomedical Engineering and the Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China School of Biomedical Engineering the Guangdong Provincial Key Laboratory of Medical Image Processing and the Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou China

Megavoltage computed tomography (MVCT) on the tomotherapy system has been widely used as a tomographic imaging modality for image-guided radiotherapy. However, the quality of MVCT images is often compromised by poor tissue contrast and significant noise. Conventional networks designed to enhance CT quality typically require the clean ground-truth images, which are not feasible for MVCT. In this study, we introduce a semi-supervised framework named Semi-Diff, which leverages the denoising diffusion probabilistic model and the prior information sourced from kilovoltage computed tomography (KVCT) to address challenges in MVCT enhancement. Specifically, employing a discriminative prior learning method, we first learn a mapping function to estimate MVCT noise and perform state matching. With this state matching dictionary, we then represent the MVCT image as a sample from an intermediate posterior distribution within the diffusion Markov chain, which enables the reverse conditional sampling process of the diffusion model to start directly from the noisy MVCT images. To fully explore the prior information from the plan KVCT images of the same patients, we introduce a novel diffusion base network called RefNet, whose dynamic feature aggregation module can extract and align the relevant features from reference KVCT image to enhance image restoration performance. Quantitative evaluations using simulated digital phantom data show that the proposed Semi-Diff model achieves the average FSIM score of 0.954, PSNR score of 33.22 dB, and RMSE value of 0.023, demonstrating improvements of approximately 2.16% in FSIM, 0.59% in PSNR, and a reduction of 3.58% in RMSE compared to the best-performing baseline method. Results from physical phantom and patient data further validate the model’s superior performance in noise suppression and structural preservation.

关键词： Noise Noise measurement Diffusion models Noise reduction Medical diagnostic imaging image restoration Computed tomography Feature extraction Training Biological tissues

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Ferroelectric control of spin-channel polarity in two-dimensional Sc2CO2/VSi2P4 van der Waals multiferroic heterostructures and application to nonvolatile logic-in-memory devices

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Physical Review Applied 2025年第2期23卷 024041-024041页

作者： Zhi Yang Xin-Yu Fu Shen-Ao Qin Bing-Xin Liu Chuan-Kui Wang Zong-Liang Li Shandong Key Laboratory of Medical Physics and Image Processing & Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and Electronics Shandong Normal University Jinan 250358 China

The ferroelectric (FE) control of the polarity of spin channels is crucial for designing multifunctional and high-performance spintronic devices. Here, based on first-principles calculations, we propose an effective strategy to achieve full-electrical FE control of the spin-polarized direction of the VSi2P4 layer by stacking Sc2CO2/VSi2P4 heterobilayers van der Waals (vdW) multiferroic structures. Our calculations show that the magnetization direction reversal of the VSi2P4 layer can be modulated by FE control due to the polarization-field-induced band-structure shift and interfacial charge transfer. On this basis, we design a lateral multiferroic tunnel junction (MFTJ) device with the VSi2P4 monolayer acting as the tunneling barrier, which can realize perfect bipolar spin-polarized current and spin-rectification ratio up to 4.0 × 105. The underlying mechanism is attributed to the overlap of the spin-dependent density of states in the two electrodes manipulated by FE polarization. Moreover, using the left and right FE layers as two inputs and considering the inverse spin Hall effect as the output, nonvolatile full-electrical writing and reading of magnetization states as well as multifunction spin-logic operations (and, nor, and xnor) are realized. Besides, we further design another MFTJ with the Sc2CO2 monolayer acting as the tunneling barrier, which demonstrates the tunneling electroresistance (TER) ratio increases with the tunneling barrier length and a giant TER ratio of up to 4.6 × 1021% is achieved. Our findings provide viable strategies to realize nonvolatile high-performance storage and computing functions for spintronic devices based on the Sc2CO2/VSi2P4 vdW multiferroic heterostructures.

关键词： Van der Waals forces

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AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment

arXiv

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arXiv 2025年

作者： Cao, Yuqin Min, Xiongkuo Gao, Yixuan Sun, Wei Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai China

Many video-to-audio (VTA) methods have been proposed for dubbing silent AI-generated videos. An efficient quality assessment method for AI-generated audio-visual content (AGAV) is crucial for ensuring audio-visual quality. Existing audio-visual quality assessment methods struggle with unique distortions in AGAVs, such as unrealistic and inconsistent elements. To address this, we introduce AGAVQA, the first large-scale AGAV quality assessment dataset, comprising 3, 382 AGAVs from 16 VTA methods. AGAVQA includes two subsets: AGAVQA-MOS, which provides multi-dimensional scores for audio quality, content consistency, and overall quality, and AGAVQA-Pair, designed for optimal AGAV pair selection. We further propose AGAV-Rater, a LMM-based model that can score AGAVs, as well as audio and music generated from text, across multiple dimensions, and selects the best AGAV generated by VTA methods to present to the user. AGAV-Rater achieves state-of-the-art performance on AGAVQA, Text-to-Audio, and Text-to-Music datasets. Subjective tests also confirm that AGAV-Rater enhances VTA performance and user experience. The project page is available at https://***. Copyright © 2025, The Authors. All rights reserved.

关键词： Subjective testing

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LCIQA: A Lightweight Contrastive-Learning Framework for image Quality Assessment via Cross-Scale Consistency Minimization

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Feng, Chenxi Min, Xiongkuo Ye, Long Yang, Yinghao Communication University of China State Key Laboratory of Media Convergence and Communication Beijing100024 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai200240 China Ministry of Education and School of Data Science and Media Intelligence Beijing100024 China

Blind image quality assessment (BIQA), which functions without the need for a reference image, is a challenging yet essential task in various image processing systems and downstream vision applications, ranging from semantic recognition to image enhancement. Traditionally, numerous BIQA models have been developed using supervised learning methodologies, which rely heavily on the availability and quality of ground truth data. To improve the generalization capability and robustness of these models, recent studies have explored the application of contrastive learning, aiming to enhance the quality representation capacity of model backbones through a self-supervised approach. However, the training process for contrastive learning is computationally intensive, posing significant challenges in resource-constrained environments. To mitigate this issue, we propose a Lightweight Contrastive-learning-based IQA (LCIQA) framework, designed to be efficiently trained on a single GPU without relying on ground truth data. This framework maintains a fixed vision backbone and focuses on optimizing the parameters of subsequent IQA heads through contrastive learning. To accommodate a lightweight framework, we incorporate a quality task adapter to eliminate semantic biases introduced by the features extracted from the fixed-parameter backbone. A coarse-to-fine contrastive learning strategy is then employed to train the quality regression module. Extensive experiments demonstrate the superior performance of our model in terms of both accuracy and complexity. In addition, ablation studies validate the effectiveness of each component within the proposed framework. © 1991-2012 IEEE.

关键词： Self-supervised learning

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Mamba-based spatial-spectral fusion network for hyperspectral unmixing

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Journal of King Saud University Computer and Information Sciences 2025年第3期37卷 1-24页

作者： Gan, Yuquan Wei, Jingtao Xu, Mengmeng School of Telecommunication and Information Engineering Xi’an University of Posts & Telecommunications Xi’an China Xi’ an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts & Telecommunications Xi’an China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an China

Hyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values. Recently, the Mamba model has gained significant attention for its exceptional performance in natural language processing and has been extended to vision research. With its strong capability for long-range modeling and linear computational complexity, Mamba demonstrates substantial potential in hyperspectral image processing. However, due to the intrinsic requirement of HU tasks for comprehensive integration of spatial and spectral information, challenges remain in effectively leveraging Mamba for hyperspectral representation. To address these issues, we propose a novel Mamba-based spatial-spectral fusion network for hyperspectral unmixing (Mamba-SSFN). This network introduces a fusion mechanism to jointly learn spectral and spatial feature representations, enabling more efficient extraction of critical HSI features. Specifically, in the spatial feature extraction module, we integrate multi-scale analysis with the Mamba module, enabling the capturing of both local and global spatial information. In the spectral feature extraction module, the Mamba module is employed in a grouped manner to process spectral vectors, exploring the correlations among different spectral groups. Finally, an effective fusion mechanism is implemented to integrate spatial and spectral *** results demonstrate that Mamba-SSFN achieves outstanding performance across multiple benchmark datasets, significantly surpassing existing state-of-the-art methods in terms of unmixing accuracy, model robustness, and computational efficiency.

关键词： Fusion mechanism Hyperspectral image Hyperspectral unmixing Mamba model Multi-Scale analysis

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional Control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Multi-task Guided Blind Omnidirectional image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： image quality

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Low-light image enhancement algorithm using a residual network with semantic information

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 52-62,84页

作者： Duan Lian Tang Guijin Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China

Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images,a low-light image enhancement(LIME)algorithm based on a residual network was *** algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different ***,a composite loss function was also designed for the process of low illumination image enhancement,which dynamically evaluated the loss of an enhanced image from three factors of color,structure,and *** ensures that the model can correctly enhance the image features according to the image semantics,so that the enhancement results are more in line with the human visual *** results show that compared with the state-of-the-art algorithms,the semantic-driven residual low-light network(SRLLN)can effectively improve the quality of low illumination images,and achieve better subjective and objective evaluation indexes on different types of images.

关键词： image enhancement convolutional neural network(CNN) residual learning image semantics

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular Technology 2025年

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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Study of frame-rate up conversion based on H.264

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The Journal of China Universities of Posts and Telecommunications 2007年第1期14卷 106-110页

作者： GAN Zong-liang ZHU Xiu-chang Province Key Laboratory on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

In this study, a low complexity frame-rate up conversion method using compressed domain information for H.264 decoder is proposed. In the proposed scheme, the motion vectors （MVs） are estimated using constant acceleration motion model, and the MVs regarded as no credibility are corrected, and the interpolation method is applied on the basis of the macroblock （MB） coded types. Applied to the H.264 decoder, the proposed method provides high quality interpolation frames and an obvious decrease of the block artifacts.

关键词： video post processing H.264 motion compensated frame interpolation (MCFI) frame-rate up conversion (FRUC)

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