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16th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th International conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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ENHANCE GENERATIVE ADVERSARIAL NETWORKS BY WAVELET TRANSFORM TO DENOISE LOW-DOSE CT imageS

ENHANCE GENERATIVE ADVERSARIAL NETWORKS BY WAVELET TRANSFORM...

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IEEE International conference on image processing (ICIP)

作者： Su, Wanqi Qu, Yili Deng, Chufu Wang, Ying Zheng, Fudan Chen, Zhiguang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781728163956

Computed Tomography (CT) has been widely used in clinical diagnosis, while its potential risk of X-ray radiation has attracted serious public concerns. Reconstructing high-quality images from low-dose CT devices is a promising solution. Whereas, existing methods mostly relied on the raw data of devices, and cannot be shared among different device suppliers. Inspired by the powerful learning ability of GAN and the structural information extraction ability of wavelet transform, we propose to combine the two together and design the WT-GAN, which extracts structure and noise information by wavelet transform and generates high-quality images by GAN. The two technologies are incorporated with each other by our well-designed loss functions. Experimental results show that the proposed WT-GAN achieves superior performance and can efficiently extract the noise while retaining the texture details. Furthermore, the WT-GAN is a post-processing method imposed on full-size images, thus it is easy to integrate into any CT systems.

关键词： Low-dose CT generative adversarial networks wavelet transform image denoising

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Blind CT image Quality Assessment Model Based on CT image Statistics 4

Blind CT Image Quality Assessment Model Based on CT Image St...

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4th International Symposium on image Computing and Digital Medicine, ISICDM 2020

作者： Duan, Jiayu Cai, Jianmei Zhi, Shaohua Mou, Xuanqin Xi'an Jiaotong University School of Information and Communication Engineering China

ISBN: (纸本)9781450389686

image quality assessment is widely used in many image processing tasks, which can help researchers adjust image processing algorithms, design imaging systems, and evaluate image processing systems. Generally, CT image quality assessment can be categorized into task-specific and general image quality evaluation. Task-specific image quality assessment evaluates the performance of the imaging system or the detectability of the tumor. These IQA index, for example, are modulation transfer function (MTF), signal-to-Noise Ratio (SNR), observer model, etc. General image quality assessment measures the general reconstruction image quality under different reconstruction algorithms. SSIM (Structural Similarity), Mean Squared Error (MSE), etc. are the traditional general image quality assessment indexes widely used in nowadays CT image quality assessment. The drawback of these indexes is the demand for reference images, which is not practical in the real CT system. In this paper, we design a CT image dataset, and by using this dataset, and we propose a blind image quality assessment (BIQA) model based on CT image statistics, which can be employed to measure the algorithms under no reference image situation. Different from other image datasets, we recruited no-converged images of the reconstruction process in designing datasets, which enables our BIQA model to evaluate non-converged images during the iterations. Hence, the BIQA model can be embedded in the reconstruction process to monitor reconstructed image quality during iterations. © 2020 ACM.

关键词： image quality

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Single infrared remote sensing image super-resolution via supervised deep learning 26

Single infrared remote sensing image super-resolution via su...

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conference on image and signal processing for Remote Sensing XXVI

作者： Zhang, Cong Zhang, Haopeng Jiang, Zhiguo Beihang Univ Sch Astronaut Dept Aerosp Informat Engn Beijing 102206 Peoples R China Beijing Key Lab Digital Media Beijing 102206 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simul Beijing 102206 Peoples R China

ISBN: (纸本)9781510638808

The infrared observation sensors aboard remote sensing satellites play an important role in the applications of crop yield estimation, environmental protection, land resources survey, disaster monitoring, etc. But infrared remote sensing data is always in low resolution due to hardware limitations. It is a high cost-effective choice to improve the spatial resolution of infrared remote sensing data through super-resolution (SR) algorithm. Deep learning methods have made great breakthroughs in super-resolution of natural images. In this paper, we comparably study five recently popular supervised-deep-learning-based single image SR models for the purpose of super-resolving infrared images, including SRGAN, ESRGAN, LapSRN, RCAN, and SRFBN. We first test the performance of models trained by natural images on infrared remote sensing images to obtain a benchmark, and then specially fine-tune the SR models using infrared images of Landsat8 in a transfer-learning manner. We evaluate the performance of all these fine-tuned models on infrared images with three indicators including PSNR, SSIM, and NIQE. The experimental results show that the SRFBN model achieves the best generalization ability and SR performance. Therefore, we suggest using SRFBN for super-resolution reconstruction of single infrared remote sensing image in applications.

关键词： image super-resolution deep learning infrared remote sensing supervised learning

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Real-time implementation of contextual image processing operations for 4K video stream in Zynq UltraScale plus MPSoC 12

Real-time implementation of contextual image processing oper...

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12th conference on design and architectures for signal and image processing (dasip)

作者： Kowalczyk, Marcin Przewlocka, Dominika Kryjak, Tomasz AGH Univ Sci & Technol Krakow Krakow Poland

ISBN: (纸本)9781538682371

In this paper hardware implementation of selected contextual based image pre-processing modules for a 3840x2160 @60 fps video stream in a Zynq UltraScale+ MPSoC is discussed. The following operations are considered: simple averaging (box filter), Gaussian filter, edge detection using the Sobel and Canny methods, median filter and morphological erosion and dilation operations. The scheme for implementing contextual based operations for a video stream in the format of 2 and 4 pixels per clock and challenges related to the pipelined implementation of processing such data are described. Also the use of logic resources and energy efficiency of modules described in the Verilog hardware description language and using the High Level Synthesis tools (Vivado HLS, SDSoC and xfOpenCV library) are compared. All designed modules support real-time processing of a 4K@60 fps video stream.

关键词： FPGA Zynq SoC image pre-processing contextual based filtering real-time processing Sobel Canny Vivado HLS SDSoC xfOpenCV

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Retinal-Layer Segmentation Using Dilated Convolutions 3rd

Retinal-Layer Segmentation Using Dilated Convolutions

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3rd International conference on Computer Vision and image processing (CVIP)

作者： Reddy, T. Guru Pradeep Ashritha, Kandiraju Sai Prajwala, T. M. Girish, G. N. Kothari, Abhishek R. Koolagudi, Shashidhar G. Rajan, Jeny Natl Inst Technol Karnataka Dept Comp Sci & Engn Surathkal India Pink City Eye & Retina Ctr Jaipur Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder-decoder architecture to accurately segment the prominent retinal layers.

关键词： Retinal layer segmentation Optical coherence tomography Dilated convolutions Deep learning Retina

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Fast Fractional Motion Estimation Hardware design of Inter Predication for AVS2 5

Fast Fractional Motion Estimation Hardware Design of Inter P...

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5th IEEE International conference on signal and image processing, ICSIP 2020

作者： Yang, Rongbing Hong, Yu Xiang, Guoqing Liu, Kaiyang Yan, Wei Peking University School of Software and Microelectronics China Peking University School of Electronics Engineering and Computer Science Beijing China

ISBN: (纸本)9781728168968

Fractional Motion Estimation (FME) is a very significant part of video coding technology, but its high computational complexity often limits the real-time performance and makes the area cost very high in hardware design. In this brief, this paper puts forward some novel joint optimizations with taking both algorithm and architecture into account to increase the throughput and reduce the area cost as much as possible, including SATD-based inter coding unit (CU) early split method and pipeline the 25 points parallel search and interpolation. In addition, to further improve throughput, for CUs with size greater than 8x8, we adopt the method of splicing to further reduce redundant operations and improve the ability of architecture reuse. These ideas help us save nearly 75% of the computational effort and complexity in FME of inter frame prediction. Finally, verified by the Xilinx Vivado HLS, the experiment results show that the algorithms and architecture proposed in this paper are able to process videos of 1920x1080@60 fps under the 300 MHz clock frequency with 3% bitrates increase in average. © 2020 IEEE.

关键词： Pipelines

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conference Support and Sponsors

Conference Support and Sponsors

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conference on design and architectures for signal and image processing

The conference organizers greatly appreciate the support of the various corporate sponsors listed.

ISBN: (数字)9781728140742

ISBN: (纸本)9781728140759

The conference organizers greatly appreciate the support of the various corporate sponsors listed.

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从U-Net到Transformer:深度模型在医学图像分割中的应用综述

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计算机应用 2024年第S01期44卷 204-222页

作者：张玮智于谦苏金善乎西旦·居马洪林玲伊犁师范大学网络安全与信息技术学院新疆伊宁835000 伊犁师范大学智能计算研究与应用重点实验室新疆伊宁835000 山东女子学院数据科学与计算机学院济南255300 伊犁师范大学电子与工程学院新疆伊宁835000 伊犁师范大学振动信号俘获及智能处理实验室新疆伊宁835000

精准分割医学图像中的病灶对医生探寻病因和制定诊疗方案起关键作用,计算机视觉技术的发展促使深度学习在医学图像分割领域衍生出多种模型架构。U-Net架构以其巧妙的跳跃连接、易于优化的模块设计成为这一领域的基准模型。然而,U-Net以... 详细信息

精准分割医学图像中的病灶对医生探寻病因和制定诊疗方案起关键作用,计算机视觉技术的发展促使深度学习在医学图像分割领域衍生出多种模型架构。U-Net架构以其巧妙的跳跃连接、易于优化的模块设计成为这一领域的基准模型。然而,U-Net以卷积神经网络(CNN)为主干,在长期建模依赖关系方面只擅长获取局部特征,基于CNN的各项方法在执行分割任务中缺乏对图像长期相关性的解释,无法提取全局特征。为帮助本领域学者了解U-Net的发展历程及研究现状,以问题为导向对2016-2023年U-Net改进工作进行综述。首先,从改进结构位置的角度对U-Net及其各项改进模型进行叙述,探讨各工作的研究目的和创新设计及不足之处;其次,对Transformer与U-Net的结合方式进行分析,从中获取改进工作的研究动向;最后,在Synapse和ACDC数据集上进行对比实验,通过实验分析和可视化结果表明,Transformer方法在分割精度方面有显著优势,特别是混合网络子块的结合方式,在确保模型性能的同时兼顾效率,证明了该类工作有着广阔的发展前景和研究价值。

关键词：医学图像分割 U-Net 结构改进 Transformer 深度神经网络

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design Flow for Portable Dataflow Programming of Heterogeneous Platforms 12

Design Flow for Portable Dataflow Programming of Heterogeneo...

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12th conference on design and architectures for signal and image processing (dasip)

作者： Boutellier, Jani Lunnikivi, Henri Tampere Univ Technol Lab Pervas Comp Tampere Finland

ISBN: (纸本)9781538682371

The processing platforms of contemporary mobile devices are commonly built around System-on-Chip (SoC) solutions that contain general purpose processor cores (GPPs), digital signal processors, accelerator circuits, and possibly a graphics processing unit (GPU) as processing resources. Software design for such SoCs can be very time-consuming, as the various processing resource types (e.g. GPPs and GPUs) conventionally require different languages to be programmed. For example, GPUs are programmed via CUDA or OpenCL, whereas GPPs are commonly programmed in C++. As a consequence, code that has originally been written for one processing resource, cannot necessarily be executed on a different processing resource type. This paper presents a novel design flow that addresses this code portability challenge. On a high level the application is described using a dataflow graph, whereas the detailed functionality of application components is written in Halide, a performance portable language that provides code generation for OpenCL, CUDA, HVX DSP, ARM and x86 targets. The proposed design flow is built around PRUNE, a recent dataflow programming framework. The functionality of the design flow is presented with three case study applications, and the measurements show an average speedup of 9.3x over single-core C code when the proposed design flow is used.

关键词： dataflow programming parallel processing digital signal processing

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