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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Matilde Costa Anoek Strumane Annelies Raes Ann Van Soom Danilo Babin Jan Aelterman Department of Chemical Engineering Faculty of Engineering University of Porto Porto Portugal Department of Telecommunications and Information Processing - Image Processing and Interpretation Ghent University-Imec Ghent Belgium Department of Internal Medicine Reproduction and Population Medicine Faculty of Veterinary Medicine Ghent University Merelbeke Belgium

The success rate of bovine in vitro embryo reproduction is low and highly dependent on the oocyte quality. The selection of the oocyte to be fertilized is done by the embryologists’ visual examination of oocytes. It is time-consuming, subjective, and inconsistent between specialists in the area. In this paper, a semi-automatic solution is proposed to score the quality of an immature oocyte. It consists of a deep learning model to classify oocyte competence. The model was trained and tested with real data, composed of images of immature oocytes and their label of whether they developed into blastocysts after fertilization. To the best of our knowledge, automated bovine oocyte classification was not attempted before, but experimental results show that our proposed solution is more robust and objective than specialists’ visual assessment and comparable with other works on human *** relevance— This establishes a semi-automatic real-time method to score bovine immature oocytes, based on stereo-microscopy images. Our method will significantly reduce the time of in vitro embryo production and its success.

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Blind image Super-Resolution via Domain Translation and Diffusion Models

Blind Image Super-Resolution via Domain Translation and Diff...

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Natural Language processing (ICNLP), International Conference on

作者： Ying Zhang Yinghua Li Zifei Zhang Chaojun Zhang Jinglu He Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China Research and Development Department Southwest Technology and Engineering Research Institute Chongqing China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Blind image super-resolution is gaining increasing attention due to its significant practical implications. Poor model generalization results from the assumption that low-resolution (LR) images are the consequence of bicubic downsampling of high-resolution (HR) images, which is made by a number of existing super-resolution methods. We employ a degradation model similar to BSRGAN to synthesize LR images, making them appear more realistic, and then treat the blind image super-resolution problem as a domain translation issue, utilizing diffusion models for resolution enhancement.

关键词： Degradation Superresolution Diffusion models Natural language processing image reconstruction

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Localization of Just Noticeable Difference for image Compression

Localization of Just Noticeable Difference for Image Compres...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Guangan Chen Hanhe Lin Oliver Wiedemann Dietmar Saupe Image Processing and Interpretation (IPI) imec research group at Ghent University Belgium School of Science and Engineering University of Dundee United Kingdom Department of Computer and Information Science University of Konstanz Germany

The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given reference image and a compression algorithm represents the minimal level of compression that causes noticeable differences in the reconstruction. These differences can only be observed in some specific regions within the image, dubbed as JND-critical regions. Identifying these regions can improve the development of image compression algorithms. Due to the fact that visual perception varies among individuals, determining the PJND values and JND-critical regions for a target population of consumers requires subjective assessment experiments involving a sufficiently large number of observers. In this paper, we propose a novel framework for conducting such experiments using crowdsourcing. By applying this framework, we created a novel PJND dataset, KonJND++, consisting of 300 source images, compressed versions thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and 129 self-reported locations of JND-critical regions for each source image. Our experiments demonstrate the effectiveness and reliability of our proposed framework, which is easy to be adapted for collecting a large-scale dataset. The source code and dataset are available at https://***/angchen-dev/LocJND.

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A Modified E-Shaped Compact Printed Antenna for 28 GHz 5G Applications 6th

A Modified E-Shaped Compact Printed Antenna for 28 GHz 5G A...

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6th International Conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2020

作者： Ghazaoui, Yousra El Alami, Ali Das, Sudipta El Ghzaoui, Mohammed Intelligent Systems Networks and Telecommunications Team IMAGE Laboratory EST-UMI Meknes Morocco Faculty of Sciences and Technics Renewable Energies Information Processing and Transmission Laboratory Moulay Ismail University Errachidia Morocco Department of Electronics and Communication Engineering IMPS College of Engineering and Technology Malda India

ISBN: (纸本)9789813368927

A single band printed antenna with a modified E-shaped structure is proposed for 5G applications at 28 GHz band. The suggested antenna has been constructed on a 5.5 × 4.35 mm2 FR-4 substrate (Εr= 4.4 and h= 1.6 mm ) through slot loading technique. The incorporation of rectangular and tiny square shaped slots has improved the resonance and radiation characteristics of the designed antenna. The wide operating bandwidth of the suggested antenna covers 25.99–29.88 GHz frequency spectrum to support 28 GHz 5G applications. The presented antenna depicts low mismatch loss and thus better impedance matching offering a reflection coefficient of about −40.88 dB at 28 GHz. The proposed antenna offers desired radiation patterns (E and H planes) with a peak gain of about 5.64 dBi at 28 GHz. Furthermore, an extensive analysis of VSWR, input impedance, and surface current distribution are also presented to explain the working functionality of the suggested antenna. The proposed structure is a preferable choice for 28 GHz 5G applications due to its compact size and high performance parameters and it covers the bandwidth requirements of 5G applications at 28 GHz. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Microstrip antennas

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Temporal and Spatial Perception: A Novel Perceptual Rate-Distortion Optimization Method for H.266/VVC Encoding

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Ai, Da Wang, Jiahao He, Ting Yuan, Hui Liu, Ying Ling, Nam Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Xi'an710121 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shenzhen-Research Institute Shenzhen518057 China Santa Clara University Department of Computer Science and Engineering Santa ClaraCA95053 United States

Introducing saliency information to mitigate perceptual redundancy and achieve superior compression represents a novel approach to the development of video compression. Existing saliency-based compression coding methods rely on the determination of saliency regions and focus too much on saliency regions while ignoring the perceptible distortion in non-saliency regions. We propose a spatiotemporal visual perceptual rate-distortion optimization (PRDO) algorithm for Versatile Video Coding (H.266/VVC) that is more in line with the human visual system (HVS). Firstly, we establish a linear weighted distortion model based on spatiotemporal and saliency features. The distortion model makes effective use of saliency features while considering image content in non-saliency regions that is still perceptible to the human eye, thereby achieving an overall visual effect that conforms to human subjective perception. Based on this distortion model, we propose a saliency adaptive quantization parameter (SAQP) selection method with a more flexible quantization parameter selection range, adaptively allocating the optimal coding unit quantization parameter according to the saliency regions of the image, ensuring a balanced bitrate allocation between saliency and non-saliency regions. The proposed method is implemented for the first time on the H.266/VVC coding standard, attaining an average bitrate saving of 19.9% across all test sequences and an average PSNR improvement of 2.65 dB in saliency regions compared to VTM16.0. The BD-EWPSNR of the proposed PRDO and SAQP method improves by 1.34 dB and 1.45 dB in the All-Intra and Lowdelay-P encoding modes, respectively. Additionally, the BD-Rate based on EWPSNR is reduced by 25.86% and 33.73%, respectively, with an overall compression coding time saving of 19.76%. The experimental results demonstrate that the proposed method can significantly reduce the bit rate and coding time while improving the subjective perceptive quality, providing a c

关键词： Signal distortion

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Human action recognition using hierarchic body related occupancy maps

Human action recognition using hierarchic body related occup...

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作者： Roegiers, Sanne Allebosch, Gianni Veelaert, Peter Philips, Wilfried Department of Telecommunications and Information Processing Image Processing and Interpretation Ghent University St-Pietersnieuwstraat 41 Ghent9000 Belgium IMEC Leuven3001 Belgium

This paper introduces a novel spatial method for human action recognition that is discriminative without needing temporal information or action key poses. First, skeletal data is acquired with the Microsoft Kinect v2 sensor and undergoes a Pose Invariant Normalization (PIN) process. The PIN process translates, rotates and scales the various observed poses to eliminate body differences and positional differences between subjects. Second, the method uses a Body Related Occupancy Map (BROM), that describes in a 3D grid how the area around specific body parts is used, as a strong indicator of the particular action that is being performed. The BROM and its 2D projections are used as feature inputs for Random Forest classifiers. These classifiers are then combined in a hierarchic structure to boost the classification performance. The approach is tested on a self-captured database of 23 human actions for game-play. On this database a classification with an accuracy score of 91% is achieved for the hierarchic BROM (HiBROM) classification. On the public CAD60 dataset, the HiBROM classifier attains 87.2% accuracy which is comparable to other state-of-the-art methods. © 2019-IOS Press and the author(s). All rights reserved.

关键词： Classification (of information)

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A Deep Learning-Based Approach for Defect Detection and Removing on Archival Photos 18

A Deep Learning-Based Approach for Defect Detection and Remo...

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18th image processing: Algorithms and Systems Conference, IPAS 2020

作者： Sizyakin, R. Voronin, V. Gapon, N. Zelensky, A. Pižurica, A. Department Telecommunications and Information Processing IPITELIN-imec Ghent University Ghent Belgium Moscow State University of Technology "STANKIN" Moscow Russia Lab. "Mathematical methods of image processing and intelligent computer vision systems" Don State Technical University Rostov-on-Don Russia

Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper proposed a system for detection and removing image defects based on machine learning. The method for detecting damage to an image consists of two main steps: the first step is to use morphological filtering as a pre-processing, the second step is to use the machine learning method, which is necessary to classify pixels that have received a massive response in the preprocessing phase. The second part of the proposed method is based on the use of the adversarial convolutional neural network for the reconstruction of damages detected at the previous stage. The effectiveness of the proposed method in comparison with traditional methods of defects detection and removal was confirmed experimentally. © 2020, Society for Imaging Science and Technology

关键词： Defects

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Asymmetry Total Variation and Framelet Regularized Nonconvex Low-Rank Tensor Completion

SSRN

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SSRN 2022年

作者： Chen, Yongyong Zhao, Xiaojia Xu, Tingting Zeng, Haijin Xu, Yanhui Chen, Junxing The School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Image Processing and Interpretation Imec Research Group Ghent University Belgium Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China The College of Medicine and Biological Information Engineering Northeastern University Shenyang110004 China

The low-rank tensor representation has shown enormous potential and advantages in diverse applications. However, (1) due to the inherent limitations of the low-rank tensor model, the tensor nuclear norm is utilized yet as a biased estimate of the tensor rank, which limits the image recovery performance. (2) most current low-rank approximation-based methods focus on the preservation of global information, while ignoring the local details preservation such as the spatial piece-wise smoothness and sharp edges. To overcome he above obstacles, we propose a novel asymmetry three-dimensional total variation and framelet regularized nonconvex low-rank tensor completion (ATV-FNTC) model, which integrates the nonconvex penalty function and asymmetric three-dimensional total variation into one unified model. Specifically, ATV-FNTC introduces one nonconvex penalty function as a tighter regularizer to approximate the tensor rank. Different from existing methods, the asymmetric three-dimensional total variation regularization is developed to achieve more accurate recovery of detailed information and flexibly control the smoothing strength of different dimensions of tensor data. Furthermore, we design an iterative algorithm to solve the nonconvex ATV-FNTC model by the alternating direction methods of multipliers. Experimental results reveal the superiority of the proposed method compared with several state-of-the-art methods when handling different completion tasks. © 2022, The Authors. All rights reserved.

关键词： Tensors

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Cross-Scale Texture Supplementation for Reference-based Medical image Super-Resolution

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Li, Yinghua Hao, Weiao Zeng, Hao Wang, Longguang Xu, Jian Routray, Sidheswar Jhaveri, Rutvij H. Gadekallu, Thippa Reddy Xi'an University of Posts and Telecommunications Center for Image and Information Processing Chang'an West St. Chang'an District Xi'an China Aviation University of Air Force Jilin Changchun China Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Chang'an West St. Chang'an District Xi'an China Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology Gujarat Gandhinagar India Zhejiang A&F University The College of Mathematics and Computer Science Hangzhou China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura India

Magnetic Resonance Imaging (MRI) is a widely used medical imaging technique, but its resolution is often limited by acquisition time constraints, potentially compromising diagnostic accuracy. Reference-based image Super-Resolution (RefSR) has shown promising performance in addressing such challenges by leveraging external high-resolution (HR) reference images to enhance the quality of low-resolution (LR) images. The core objective of RefSR is to accurately establish correspondences between the reference HR image and the LR images. In pursuit of this objective, this paper develops a Self-rectified Texture Supplementation network for RefSR (STS-SR) to enhance fine details in MRI images and support the expanding role of autonomous AI in healthcare. Our network comprises a texture-specified selfrectified feature transfer module and a cross-scale texture complementary network. The feature transfer module employs highfrequency filtering to facilitate the network concentrating on fine details. To better exploit the information from both the reference and LR images, our cross-scale texture complementary module incorporates the All-ViT and Swin Transformer layers to achieve feature aggregation at multiple scales, which enables high-quality image enhancement that is critical for autonomous AI systems in healthcare to make accurate decisions. Extensive experiments are performed across various benchmark datasets. The results validate the effectiveness of our method and demonstrate that the method produces state-of-the-art performance as compared to existing approaches. This advancement enables autonomous AI systems to utilize high-quality MRI images for more accurate diagnostics and reliable predictions. © 2013 IEEE.

关键词： feature aggregation image enhancement reference-based super-resolution texture transfer vision transformer

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EWT: Efficient Wavelet-Transformer for Single image Denoising

arXiv

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arXiv 2023年

作者： Li, Juncheng Cheng, Bodong Chen, Ying Gao, Guangwei Zeng, Tieyong The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Xidian University Xian China The Department of Cyberspace Security Beijing Electronic Science & Technology Institute Beijing China The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The Department of Mathematics The Chinese University of Hong Kong New Territories Hong Kong

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public. © 2023, CC BY.

关键词： image denoising

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