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Enhancing Colonoscopy image Quality with CLAHE in the GASTROLAB Dataset 3

Enhancing Colonoscopy Image Quality with CLAHE in the GASTRO...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Karthikha, R. Najumnissa Jamal, D. B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Electrical and Electronics Engineering Chennai India B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798350343632

This research focuses on improving image quality in the context of colonoscopy, a critical procedure for diagnosing gastrointestinal conditions such as colorectal polyps, which can lead to cancer. The study highlights the challenges posed by the large volume of frames generated during colonoscopy procedures, which necessitates the use of automated systems to detect anomalies. Despite the fact that some datasets contain low-quality images, it emphasizes the importance of various datasets in gastrointestinal disease research and machine learning. The primary goal of this work is to improve the image quality of the GASTROLAB dataset, emphasizing the significance of accurate results derived from high-quality data. The study looks into various image enhancement techniques, with a focus on Contrast Limited Adaptive Histogram Equalization (CLAHE), which produces superior results in image quality metrics like Peak signal to Noise Ratio (PSNR) and), Structural Similarity Index (SSIM), which are 32.26 and 0.912, respectively. The findings demonstrate the utility of PSNR and SSIM as image quality assessment tools, while also emphasizing the importance of clinical validation and expert judgement in medical image evaluation. The study concludes with findings that support CLAHE as a preferred method for improving image quality, particularly when visualizing small polyps during colonoscopy. © 2023 ieee.

关键词： Colonoscopy Contrast Limited Adaptive Histogram Equalization (CLAHE) GASTROLAB Dataset image Enhancement Peak signal to Noise Ratio (PSNR) Structural Similarity Index (SSIM)

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CROSS DOMAIN LOW-DOSE CT image DENOISING WITH SEMANTIC INFORMATION ALIGNMENT 29

CROSS DOMAIN LOW-DOSE CT IMAGE DENOISING WITH SEMANTIC INFOR...

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ieee international conference on image processing (ICIP)

作者： Huang, Jiaxin Chen, Kecheng Sun, Jiayu Pu, Xiaorong Ren, Yazhou Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Sichuan Univ West China Hosp Chengdu Peoples R China Mianyang Cent Hosp NHC Key Lab Nucl Technol Med Transformat Mianyang Sichuan Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recently, cross domain adaptation has been applied into quite a few image restoration tasks. While promising performance has been achieved, the domain shift problem between the training set (a.k.a., source domain) and the testing set (a.k.a., target domain) in Low-dose Computed Tomography (LDCT) image denoising tasks is typically ignored by most existing methods. This is prone to the degradation of the denoising performance due to large discrepancy of feature distribution in each dataset from various vendors. Therefore, a simple yet effective LDCT denoising approach has been proposed in this paper to alleviate the domain shift between source and target domains through a novel semantic information alignment. Specifically, we first propose an adaptive version of random frequency mask (RFM) to extract the shared semantic information of cross domains. Then, we incorporate the mask into the existing denoiser to construct a semantic-information-guided objective. Experiments on synthetic and real datasets show our proposed method achieves impressive performance.

关键词： Medical image denoising Low-dose CT Domain adaptation Deep learning

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EXPECTATION CONSISTENT PLUG-AND-PLAY FOR MRI 47

EXPECTATION CONSISTENT PLUG-AND-PLAY FOR MRI

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47th ieee international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Shastri, Saurav K. Ahmad, Rizwan Metzler, Christopher A. Schniter, Philip Ohio State Univ Dept ECE Columbus OH 43210 USA Ohio State Univ Dept BME Columbus OH 43210 USA Univ Maryland Dept CS College Pk MD 20742 USA

ISBN: (纸本)9781665405409

For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network. Although such methods have been successful, they can be improved. For example, the denoiser is often trained using white Gaussian noise, while PnP's denoiser input error is often far from white and Gaussian, with statistics that are difficult to predict from iteration to iteration. PnP methods based on approximate message passing (AMP) are an exception, but only when the forward operator behaves like a large random matrix. In this work, we design a PnP method using the expectation consistent (EC) approximation algorithm, a generalization of AMP, that offers predictable error statistics at each iteration, from which a deep-net denoiser can be effectively trained.

关键词： Magnetic resonance imaging Message passing Gaussian noise Neural networks signal processing algorithms signal processing Prediction algorithms

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DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for image Classification 15

DNAT: Multi-scale Transformer with Dilated Neighborhood Atte...

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15th ieee international conference on Wireless Communications and signal processing, WCSP 2023

作者： Jiang, Chenfeng Zhou, Quan Mo, Zhiyi Wang, Jing Fan, Yawen Wu, Xiaofu Zhang, Suofei Kang, Bin Nanjing University of Posts & Telecommunications National Engineering Research Center of Communications and Networking China Wuzhou University Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou China Nanjing University of Posts & Telecommunications Department of Internet of Things China

ISBN: (纸本)9798350324662

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on imageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification. © 2023 ieee.

关键词： Dilated Neighborhood Attention image Classification Self-attention visual Transformer

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international conference on Advanced Technologies for signal and image processing, ATSIP 2022

International Conference on Advanced Technologies for Signal...

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6th international conference on Advanced Technologies for signal and image processing, ATSIP 2022

ISBN: (纸本)9781665451161

The proceedings contain 54 papers. The topics discussed include: epileptic seizures detection on EEG signal using deep learning techniques;a new automatic method for human coding and non-coding zones characterization and classification based on FCGR coding and CNN classifier;involving FCGR method in multiclass cancer diseases classification with transfer learning models;deep learning for seizure prediction using intracranial electroencephalogram;structured case base knowledge using unsupervised learning;improving smart city frameworks based on enterprise architecture with territorial governance to manage covid -19 crisis;hypothetical reasoning method based on hypothetical cases;and identification of the dynamic parameters of the planar robot model at 2 degrees of freedom using the least squares method.

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th international conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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Design and VLSI Implementation of Hybrid Analog to Digital Converter for High Frequency applications

Design and VLSI Implementation of Hybrid Analog to Digital C...

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2023 ieee international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Kumar, Keerthan Bhatt, Punyashree Shetty, Prathap S Chandra, Vinod Kumar, Ashwin Sahyadri College of Engineering and Management Department of Electronics and Communication Engineering Mangaluru India

ISBN: (纸本)9798350340129

The users in a wire-free era are benefiting from the advancement of wireless technologies. The transmitter and receiver of the wireless communication systems use digital signal processing. In nature all the signals are analog. In view of the analog signal's processing complexity, it is transformed to digital form to facilitate processing. Because of advancements in integrated circuit technology during the previous decade, expansion in the digital signal processor industry has been rapid. The ever-increasing speed of wireless communication networks places enormous demands on the performance of high-speed low-resolution analog-to-digital converters. Analogue signals are complicated to process, so they are transformed to digital form to make processing simpler. Over the past ten years, advancements in integrated circuit technology have accelerated the development of digital signal processors. As a result, an interface between systems for processing digital and analogue signals is provided by analog-to-digital converters. Good analog-to-digital converters must meet extremely high demands due to the wireless communication systems' ongoing advancement in speed. This research deals with modified 3-bit Flash Analog- to- Digital Converter (ADC). This report's converter is built using a two-step flash approach. An efficient method for creating analog-to-digital converters is the two-step flash process. The converter's sampling frequency is around 2 MHz, and its integral and differential nonlinearities are both less than 1 LSB with a power dissipation of under 40 mW. By creating a software reference model with MATLAB Simulink, the pre design and verification of the redesigned flash ADC were completed. The updated flash converter sub-blocks' CMOS level schematic diagram was recorded and created in Cadence Virtuoso utilizing 180nm technology and an operational voltage of 1.8 volts. Cadence has verified the designed ADC. © 2023 ieee.

关键词： Digital signal processors

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An Efficient Anaglyph 3D Content based Video Retrieval using Watermarking Technique 22

An Efficient Anaglyph 3D Content based Video Retrieval using...

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22nd international conference on Cyberworlds, CW 2023

作者： Dhaou, Dorra Jabra, Saoussen Ben Zagrouba, Ezzeddine University Tunis El Manar Higher Institute of Computer Science ImTic Lab Ariana Tunisia University of Sousse National Engineering School of Sousse LimTic Lab Sousse Tunisia

ISBN: (纸本)9798350315653

Digital watermarking challenges are multiple. The most important are copyright protection, integrity control, and indexing. The latter has gained significant attention from the research community due to the increasing production of multimedia data. Existing anaglyph 3D video watermarking methods are developed mainly for copyright protection applications but they have not targeted indexing applications that facilitate the retrieving of anaglyph 3D videos in databases. Hence, it is necessary to develop a robust watermarking technique dedicated to indexing in order to both protect and facilitate retrieving anaglyph 3D videos. This paper presents a novel watermarking-based retrieval system for anaglyph 3D videos. The system utilizes a mosaic image generated from the original video as the signature, providing a comprehensive representation of the video's content. To handle the large size of the mosaic image, it is divided into a set of vectors. The obtained vectors are embedded into different selected frames using a dynamic insertion key to ensure visual quality and robustness against collusion attacks. During retrieval, the extracted signatures from the database are compared with the query video's mosaic image using a similarity measure. Experimental results demonstrate high visual quality, robustness, and effectiveness of the proposed system. © 2023 ieee.

关键词： Query processing

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SLTFILL: SPATIAL AND LIGHT TRANSFORMER FOR MULTI-REFERENCE image INPAINTING 29

SLTFILL: SPATIAL AND LIGHT TRANSFORMER FOR MULTI-REFERENCE I...

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29th ieee international conference on image processing, ICIP 2022

作者： Fan, Yuliang Zℎou, Yue Yang, Zonghao Tong, Zhenyu Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

image inpainting aims to restore the missing regions of corrupted images plausibly. Recent techniques of image inpainting handle it as a pure generation problem and utilize large-scale training data to learn semantic and texture information to fill the missing area. However, generative networks frequently distort local structures or introduce visible artifacts while filling large missing regions. To address this issue, we propose SLTFill, an image inpainting method that uses a set of reference images which share scene contents with the corrupted image to guide the synthesis of missing regions. A key challenge addressed by our approach is aligning the transformations of the reference images with the context of the corrupted image and making sure they are consistent in spatial structure and light space. Qualitative and quantitative experiments demonstrate the state-of-the-art performance of our approach on the image inpainting task. © 2022 ieee.

关键词： Deep learning

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ROBUST THERMAL INFRARED PEDESTRIAN DETECTION BY ASSOCIATING VISIBLE PEDESTRIAN KNOWLEDGE 47

ROBUST THERMAL INFRARED PEDESTRIAN DETECTION BY ASSOCIATING ...

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47th ieee international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Park, Sungjune Choi, Dae Hwi Kim, Jung Uk Ro, Yong Man Korea Adv Inst Sci & Technol Sch Elect Engn Image & Video Syst Lab Daejeon South Korea Samsung Elect Suwon South Korea

ISBN: (纸本)9781665405409

Recently, pedestrian detection on thermal infrared images has shown the robust pedestrian detection performance. In this paper, we propose a novel thermal infrared pedestrian detection framework which can associate and utilize the complementary pedestrian knowledge from visible images. Motivated by that humans can associate useful information from other sensors to perform a more reliable decision, we devise a Visible-sensory Pedestrian Associating (VPA) Memory to conduct the robust pedestrian detection by utilizing complementary visible-sensory pedestrian knowledge explicitly. The VPA Memory is trained to store the pedestrian information of visible images and associate it with a given thermal infrared pedestrian knowledge via the memory associating learning. We verify the effectiveness of the proposed framework with extensive experiments, and it achieves state-of-the-art pedestrian detection performance on thermal infrared images.

关键词： Thermal infrared pedestrian detection Complementary visible-sensory pedestrian knowledge Visible-sensory Pedestrian Associating (VPA) Memory

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