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Dual Encoders for Diffusion-based image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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An Block image Encryption Algorithm based on Reversible Cellular Automata 21

An Block Image Encryption Algorithm Based on Reversible Cell...

引用

21st ieee international conference on Communication technology, ICCT 2021

作者： Song, Xiaomeng Shi, Mengna Zhou, Yanqi Wang, Erfu Heilongjiang University School of Electronic Engineering Harbin China

ISBN: (纸本)9781665432061

This article proposed a block image encryption scheme based on reversible cellular automata (RCA) and chaotic systems in order to construct an image encryption scheme with great confidentiality effect and high encryption and decryption efficiency. In the preparation phase of this algorithm, the original image is transformed into the frequency domain using the method of integer wavelet transform (IWT). The IWT divides the plaintext image into four frequency blocks. The low-frequency image block containing critical information is treated differently from other high-frequency image blocks in the following procedure. In order to avoid direct processing of image blocks with visual significance, a combined one-dimensional chaotic map is used to rearrange the pixel positions within the image blocks respectively. The chaotic map is then used to acquire various RCA rules and iterations. According to the different importance of the information stored in the image block, the RCA evolution of the image block is carried out with different rules and different iteration times. Finally, the image is scrambled and diffused around the whole image to reduce the blocking effect. The algorithm blocks the images according to different importance and encrypts them at different levels. Avoiding the waste of time and resources caused by the same operation on the whole image. © 2021 ieee.

关键词： Chaotic systems

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PromptSeg: Learning to Segment Medical image via Visual Prompts

PromptSeg: Learning to Segment Medical Image via Visual Prom...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Minfan Zhao Ziqi Zhu Jun Shi Zhaohui Wang Junshi Chen Hong An Bing Yan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China The First Affiliated Hospital of USTC University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific models on unseen tasks, necessitating domain adaptation or fine-tuning. This typically involves data annotation and network retraining, limiting the application of deep learning in clinical practice. In this study, we propose PromptSeg, an innovative Transformer-based unified segmentation framework for general medical image segmentation tasks. PromptSeg aims to utilize the provided visual prompts to recognize task patterns and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets during inference, our method only requires a few annotated prompt pairs to understand the task and segment the query images without retraining, alleviating the need for large-scale annotation data. The experimental results demonstrate that our method outperforms existing state-of-the-art methods and exhibits high generalization capability on multiple unseen tasks. The source code is available at https://***/MinfanZhao/PromptSeg.

关键词： Deep learning Training image segmentation Visualization Annotations Source coding Scalability Transformers Speech processing Biomedical imaging

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INFRARED AND VISIBLE image FUSION based ON MODAL FEATURE FUSION NETWORK AND DUAL VISUAL decision

INFRARED AND VISIBLE IMAGE FUSION BASED ON MODAL FEATURE FUS...

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2021 ieee international conference on Multimedia and Expo, ICME 2021

作者： Yang, Yong Liu, Jiaxiang Huang, Shuying Wan, Weiguo Kong, Xiangkai Zhang, Wang School of Information Technology JUFE Nanchang330013 China School of Software and Internet of Things Engineering JUFE Nanchang330013 China

ISBN: (纸本)9781665438643

Infrared and visible image fusion can integrate the complementary information of infrared and visible images to realize a more accurate scene interpretation. In this paper, a novel infrared and visible image fusion framework is proposed, which is based on a modal feature fusion network (MFFN) and a dual visual decision fusion module. Firstly, the infrared and visible images are decomposed by side window filtering to obtain the modal feature components, which can emphasize the target information of the source images. Secondly, MFFN is designed to merge the modal feature components to get a modal fused image. Then, a dual visual decision fusion module is built to obtain a supplement image which can provide more visual supplementary information for the modal fused image. Lastly, the final fusion result is achieved by combining the supplement image and the modal fused image. Experimental results show that the proposed method can generate fusion results with clearer targets and richer texture details, compared with other state-of-the-art fusion methods. © 2021 ieee Computer Society. All rights reserved.

关键词： image fusion

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DWTSRNet: Discrete Wavelet Transform Super Resolution Network for medical single image super-resolution

DWTSRNet: Discrete Wavelet Transform Super Resolution Networ...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Pietro Cestola Alice Othmani Amine Lagzouli Vittorio Sansalone Luciano Teresi Department of Mathematics and Physics Roma Tre University Roma Italy LISSI Paris-Est Créteil University France School of Mechanical Medical and Process Engineering Queensland University of Technology Brisbane Australia MSME Laboratory Paris-Est Créteil University Créteil France Department of Industrial Engineering Electronics and Mechanics Roma Tre University Roma Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single image super-resolution generation is a computer vision task that aims to enhance the detail and quality of a low-resolution image, generating a higher-resolution version. To date, several attempts have been made to approach this problem using convolutional neural networks and, more recently, GANs, diffusion models, and transformers. The single-image super-resolution task largely involves the recovery of missing high-frequency details. This has led to the exploration of methods based on the Fourier and wavelet transform. In this work we focused on transformer-based models and wavelets, with the intention of exploiting the advantages of both: the former for its expressive capacity and remarkable achievements in the state of the art, the latter for its ability to decompose a signal into informative components. The proposed model, Discrete Wavelet Transform Super Resolution Network (DWTSRNet), is a transformer-based model composed of three parts: a shallow feature extraction module utilizing the discrete wavelet transform, a deep feature extraction module composed of several residual blocks of SwinV2 Transformer, and a reconstruction module using the inverse discrete wavelet transform. We have conducted experiments on the super-resolution of medical images, specifically focusing on MRI slices of the human brain. The experimental results show that DWTSRNet outperformed other wavelet-based models on standard benchmark datasets and demonstrated promising results on medical datasets. Our code is available at https://***/pcestola/DWTSRNet.

关键词： Magnetic resonance imaging Superresolution Focusing Transformers Feature extraction Discrete wavelet transforms Standards signal resolution image reconstruction Biomedical imaging

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Digital Beamforming on Receive in Elevation for Spaceborne Reflector SAR System

Digital Beamforming on Receive in Elevation for Spaceborne R...

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ieee international conference on signal and image Processing (ICSIP)

作者： Jiaqin Yang Daiyin Zhu Weijie Wu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

High spatial resolutions and wide swaths are pursued goals for spaceborne Synthetic Aperture Radar (SAR). However, these requirements are conflicting constraints on the design of traditional spaceborne SAR systems. The innovation of combining a reflector with a digital feed array offers a promising avenue to achieve high-resolution wide-swath (HRWS) imaging. Against this backdrop, this paper analyses the Scan-On-Receive (SCORE) mode and establishes the receive echo signal model of the reflector with an elevation digital feed array. To address the complexities of digital beamforming data processing within this model and compensate for the reduced echo gain from terrain height changes, this paper proposes an optimized digital beamforming (DBF) method based on the Minimum Variance Distortionless Response (MVDR) criterion. The method introduces a pivotal elevation angle correction step prior to the digital beamforming process, coupled with the incorporation of Polynomial Curve Fitting to reduce computational demands. The efficacy of the proposed method is demonstrated through simulations with both point and distributed targets.

关键词： Array signal processing Spaceborne radar Imaging Data processing Data models Polynomials Feeds

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Investigating the 3D cardiomyocyte arrangement in human interventricular septum sample using X-ray phase-contrast microtomography

Investigating the 3D cardiomyocyte arrangement in human inte...

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international conference on signal Processing Proceedings (ICSP)

作者： Wenfeng Li Zhaorui Li Yongbin Du Shunli Wang Institute of Big Data and Artificial Intelligence Zhengzhou University of Science and Technology Zhengzhou China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China School of Electrical and Control Engineering Heilongjiang University of Science and Technology Harbin China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

A good knowledge of the three-dimensional cardiomyocyte arrangement in interventricular septum is crucial for understanding both ventricle’s functions. In this preliminary study, we investigate the local myocardial architecture in a human interventricular septum sample imaged by X-ray phase-contrast micro-tomography. This imaging modality allows us to visualize the complex structure of myocardial tissue at high resolution. We first cut the reconstruction data into sub-volume of 32×32×32 voxels (i.e. 112×112×112 μm 3 ) in size, and compute the elevation angle of the local cardiomyocyte using Fast Fourier Transform based method. Then, we describe the distribution of cardiomyocyte elevation angle in the sample globally and locally. We note that, in part of regions, the running of cardiomyocyte aggregates is winding. Though the function of this specific cardiomyocyte arrangement manner is unclear, its existence is inspiring and merits further investigation.

关键词： Three-dimensional displays image resolution Fast Fourier transforms Windings Imaging Data visualization Myocardium signal processing X-ray imaging image reconstruction

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Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge

Map-Free Visual Relocalization Enhanced by Instance Knowledg...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Mingyu Xiao Runze Chen Haiyong Luo Fang Zhao Fan Wu Hao Xiong Xuepeng Ma Juan Wang Beijing University of Posts and Telecommunications Bejing China Institute of Computing Technology Chinese Academy of Sciences Bejing China Shouguang Cheng Zhi Feng Xing Technology Co. Ltd. ShanDong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Map-free visual relocalization computes camera pose using only a query image and a reference image. Therefore, it is hindered by challenges in feature-point matching and the absence of scale information in monocular images. These issues may cause significant rotational and metric errors, leading to localization failures. To address these challenges, we propose a map-free visual relocalization method enhanced with instance knowledge and depth knowledge. By utilizing instance-based matching, our approach improves the robustness of feature-point matching by focusing on relevant regions across scenes. Additionally, our depth estimation techniques provide reliable depth knowledge from a single image, improving scale recovery and reducing translation errors. Our method surpasses the previous state-of-the-art by 1.071m and 13.593° on the translation error and rotation error, respectively. Furthermore, we are also one of the winners in the Map-free Workshop & Challenge (ECCV2024), underscoring its superiority compared to concurrent approaches.

关键词： Location awareness Instance segmentation Visualization Translation Accuracy Three-dimensional displays Depth measurement signal processing Robustness Speech processing

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ECG-guided individual identification via PPG

ECG-guided individual identification via PPG

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Riling Wei Hanjie Chen Kelu Yao Chuanguang Yang Jun Wang Chao Li Zhejiang Lab Hangzhou China Hong Kong Centre for Cerebro--Cardiovascular Health Engineering (COCHE) Hong Kong China Zhejiang Lab Zhejiang University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information density. To this end, electrocardiogram (ECG) signals have been introduced as a novel modality to enhance the density of input information. Specifically, a novel cross-modal knowledge distillation framework is implemented to propagate discriminate knowledge from ECG modality to PPG modality without incurring additional computational demands at the inference phase. Furthermore, to ensure efficient knowledge propagation, Contrastive Language–image Pre-training (CLIP)-based knowledge alignment and cross-knowledge assessment modules are proposed respectively. Comprehensive experiments are conducted and results show our framework outperforms the baseline model with the improvement of 2.8% and 3.0% in terms of overall accuracy on seen- and unseen individual recognitions.

关键词： Resistance Degradation Biometrics Computational modeling Education Electrocardiography signal processing Security Speech processing Testing

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG signal with Correlation-based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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Computing, Applications and systems (COMPAS), ieee international conference on

作者： Robiul Khan Dipon Roy Md. Wajed Ali Kawsar Ahmed Refat Shekh Naziullah Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Computer Science Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection.

关键词： Computer vision Time-frequency analysis Continuous wavelet transforms Accuracy Computational modeling Fatigue Brain modeling Transformers Electroencephalography Vehicles

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