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2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Tian, Wenxin Li, Shaohui Dai, Wenrui Lu, Cewu Hu, Weisheng Zhang, Lin Du, Junfeng Xiong, Hongkai Shanghai Jiao Tong University China Tsinghua Shenzhen International Graduate School China Shanghai Cogent Technologies Co. Ltd. China Limited China

ISBN: (纸本)9798350359855

Entropy modeling plays an important role in estimating the rates of latent representations and optimizing the rate-distortion performance for learned image compression. Autoregression modules are demonstrated to eliminate spatial/channel-wise redundancy of latent representations in fixed-rate learned image compression. However, it cannot be efficiently achieved in progressive coding due to the high computational complexity raised by element-wise probability prediction. In this paper, we propose a learned progressive image compression method that enables spatial autoregression for entropy modeling. Specifically, we develop a novel codeword alignment scheme to prevent coding redundancy and achieve efficient autoregression of latent representations in different quality layers. Consequently, conditional probability estimation for the latent prediction can be achieved based on spatial autoregression in a layer-wise manner. We further extend the proposed method by dead-zone quantizers to obtain promoted rate-distortion performance. The proposed method is a successful attempt to enable spatial autoregression in learned progressive coding and further bridge the performance gap with fixed-rate models. Experimental results show that it outperforms traditional methods such as JPEG and BPG, as well as recent fine-grained learned progressive coding models DPICT and PLONQ in terms of rate-distortion performance. © 2023 ieee.

关键词： image compression

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Specific Sentiment Mask Auto-Encoder Model (S2MA) for image Sentiment Classification

Specific Sentiment Mask Auto-Encoder Model (S2MA) for Image ...

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International Joint conference on Neural Networks (IJCNN)

作者： Xing, Lehao Shi, Ge Yang, Guang Wu, Lifang Beijing Univ Technol 100 Pingleyuan Beijing 100124 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

image sentiment analysis is a domain fraught with the dual challenges of interpreting complex visual content and discerning the subtle emotional undertones it may convey. Despite the notable successes of existing visual language pretraining (VLP) models in a variety of visual tasks, they fall short in the nuanced realm of sentiment analysis. This shortfall is primarily due to their inadequate processing of sentiment-specific cues-most notably, the oversight of localized sentioment cues within images and the intricate interplay of these signals. Furthermore, these models inadequately harness the rich sentiment cues often embedded in accompanying text. In response to these shortcomings, we introduce the Specific Sentiment Mask Auto-encoder (S2MA) model, which is expressly designed to integrate sentiment information during the pretraining process. S2MA is meticulously engineered to focuse on both intermodal and intramodal sentiment cue, thereby augmenting the model's proficiency in anlysising the sentiment knowledge within visual content. Rigorous comparative evaluations of S2MA against the CLIP model, across a spectrum of downstream datasets in zero-shot and supervised learning scenarios, have validated the superiority of our approach. The empirical outcomes affirm S2MA's capacity to significantly enhance the analytical landscape of image sentiment analysis.

关键词： visual languages

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BLIND INPAINTING WITH OBJECT-AWARE DISCRIMINATION FOR ARTIFICIAL MARKER REMOVAL 49

BLIND INPAINTING WITH OBJECT-AWARE DISCRIMINATION FOR ARTIFI...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guo, Xuechen Hu, Wenhao Ni, Chiming Chai, Wenhao Li, Shiyan Wang, Gaoang Zhejiang Univ ZJU UIUC Inst Hangzhou Peoples R China Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Zhejiang Univ Sir Run Run Shaw Hosp Hangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Medical images often incorporate doctor-added markers that can hinder AI-based diagnosis. This issue highlights the need of inpainting techniques to restore the corrupted visual contents. However, existing methods require manual mask annotation as input, limiting the application scenarios. In this paper, we propose a novel blind inpainting method that automatically reconstructs visual contents within the corrupted regions without mask input as guidance. Our model includes a blind reconstruction network and an object-aware discriminator for adversarial training. The reconstruction network contains two branches that predict corrupted regions in images and simultaneously restore the missing visual contents. Leveraging the potent recognition capability of a dense object detector, the object-aware discriminator ensures markers undetectable after inpainting. Thus, the restored images closely resemble the clean ones. We evaluate our method on three datasets of various medical imaging modalities, confirming better performance over other state-of-the-art methods.

关键词： Blind image inpainting generative adversarial networks image reconstruction dense object detector

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Color image Cryptosystem Based on Sine Chaotic Map, 4D Chen Hyperchaotic Map of Fractional-Order and Hybrid DNA Coding

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ieee ACCESS 2023年 11卷 54928-54956页

作者： Alexan, Wassim Gabr, Mohamed Mamdouh, Eyad Elias, Rimon Aboshousha, Amr German Univ Cairo GUC Fac Informat Engn & Technol Commun Dept New Cairo 11835 Egypt German Int Univ GIU Math Dept Cairo 13507 Egypt German Univ Cairo GUC Fac Media Engn & Technol Comp Sci Dept New Cairo 11835 Egypt German Univ Cairo GUC Fac Basic Sci Phys Dept New Cairo 11835 Egypt German Univ Cairo GUC Fac Media Engn & Technol Digital Media Dept New Cairo 11835 Egypt Cairo Univ Sci Fac Phys Dept Giza 12613 Egypt

With advancements in computer and communication technologies, the production, utilization and applications of digital images is at an unprecedented rate. Recent applications include military communications, remote sensing, novel engineering designs storage and communications, as well as medical imaging. In most cases, such images convey highly sensitive or confidential information, which creates a strong need for the design of secure and robust color image cryptosystems. Recent literature has shown that fractional-order functions exhibit improved performance over their corresponding integer-order versions. This is especially true in their use in image processing applications. In this research work, we make use of a four-dimensional (4D) hyperchaotic Chen map of fractional-order, in conjunction with a sine chaotic map and a novel hybrid DNA coding algorithm. A thorough numerical analysis is presented, showcasing the security performance and efficiency of the proposed color image cryptosystem. Performance is gauged in terms of resilience against visual, histogram, statistical, entropy, differential, as well as brute-force attacks. Mean values of the metrics computed are as follows. MSE of 9396, PSNR of 8.27 dB, information entropy of 7.997, adjacent pixel correlation coefficient of 0, NPCR of 99.62%, UACI of 33, MAE of 80.57, and a very large key space of 2(744). The proposed image cryptosystem exhibits low computational complexity, as it encrypts images at a rate of 4.369 Mbps. Furthermore, it passes the NIST SP 800 suite of tests successfully. Comparison of the computed metrics of the proposed image cryptosystem against those reported in the state-of-the-art by counterpart algorithms show that the proposed cryptosystem exhibits comparable or superior values.

关键词： Chaos theory image processing chen hyperchaotic map DNA coding fractional-order image cryptosystem image encryption

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Development of image processing Techniques in Crack Detection and Analysis

Development of Image Processing Techniques in Crack Detectio...

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2023 International conference on Electrical, Computer, communications and Mechatronics Engineering, ICECCME 2023

作者： Al Dhafari, Luqman Sulaiman Afzal, Aftab Al Bahrani, Raid Ibrahim Al Busaidi, Yahyah Sheikh-Akbari, Akbar Hossain, Mohammad Sayeed Military Technological College Dept. Aeronautical Engineering Muscat Oman Leeds Beckett University School of Built Environment Engineering and Computing Leeds United Kingdom

ISBN: (纸本)9798350322972

Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface inspection, according to the CPCP. Scheduled inspections and regular maintenance of an aircraft through conventional methods constitute tedious and lengthy procedures. Often the visual inspections lead to subjective judgement and do not constitute repeatability. Many automated vision-based aircraft skin inspection systems have been designed to provide a safe, quick, and accurate visual assessment over the past years. This paper presents a section of research investigating defect detecting and accurately locating the outer body of an aircraft using an Unmanned Aerial Vehicle (UAV) to capture images and digital image processing techniques to locate possible cracks. The inspection system is used to initially detect locations of cracks (defects) on an aircraft's outer skin and the detected crack is further investigated using thermal and ultrasound imaging methods. The scope of this paper includes a review of the design and development of a series of advanced dedicated image processing algorithms suitable for applying digital image processing on images captured from the outer surface of a typical aircraft fuselage. © 2023 ieee.

关键词： Crack detection

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image Captioning using visual Attention and Detection Transformer Model 10

Image Captioning using Visual Attention and Detection Transf...

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10th ieee International conference on Electronics, Computing and Communication Technologies (ieee CONECCT)

作者： Eluri, Yaswanth Vinutha, N. Jeevika, M. Sree, Sai Bhavya N. Abhiram, G. Surya Dayananda Sagar Univ Bengaluru India

ISBN: (纸本)9798350385939;9798350385922

image caption generation has witnessed significant advancements with the integration of Deep Learning (DL) models. By leveraging DL techniques such as InceptionResNetV2 for feature extraction and transformer-based architectures for natural language processing, achieves remarkable results in generating descriptive captions for images. Unlike traditional Recurrent Neural Network approaches, which suffer from issues like vanishing gradients and lack of parallelization, this method offers improved efficiency and scalability. The synergy between DL models and Natural Language Models enables the system to capture intricate sematic relationships between visual content and textual descriptions, resulting in more accurate and contextually relevant captions. By integrating InceptionResNetV2 and Detection Transformer, this approach leverages the strengths of both architectures, achieving state-of-the-art performance in object detection tasks. Through joint training, the model learns to detecting images and labelling/captioning on up to 50% occluded images with Precision of 0.982, Recall of 0.931, F1 Score of 0.942 and Sensitivity of 0.892.)

关键词： Advanced Deep Learning Detection Transformer InceptionResNetV2 image Caption Occluded images

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SEAM MASK GUIDED PARTIAL RECONSTRUCTION WITH QUANTUM-INSPIRED LOCAL AGGREGATION FOR DEEP image STITCHING 49

SEAM MASK GUIDED PARTIAL RECONSTRUCTION WITH QUANTUM-INSPIRE...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feng, Chen-Bin Zhang, Jie Li, Jiaxue Zhou, Yicong Univ Macau Dept Comp & Informat Sci Macau Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In image stitching, artifacts caused by misalignment affect the visual quality and the performance of subsequent tasks such as segmentation and detection. This paper proposes SMPR, a reconstruction-based aligned image composition method to minimize artifacts. SMPR fuses images in part of the overlapping areas and reconstructs other portions from single images. Specifically, we propose a seam mask generation method to obtain optimal seam masks that pass through minimal misalignment. During training, we use the seam masks to guide the model in detecting optimal fusion areas. In testing, the model can detect fusion areas without seam masks and reconstruct stitching results. We propose a quantum-inspired local aggregation (QILA) module to improve feature reconstruction performance. We develop an encoder-decoder network with QILA and experiment on a real-world dataset. The experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative aspects.

关键词： image Stitching image Reconstruction Deep Learning image processing Quantum Neural Network

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A TWO-STAGE DEHAZING FRAMEWORK BASED ON INVERTED image CURVE-ENHANCEMENT 49

A TWO-STAGE DEHAZING FRAMEWORK BASED ON INVERTED IMAGE CURVE...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Luo, Hongwei Liu, Wei Chen, Cheng Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Due to relying on accurate estimates of the atmospheric scattering parameters for atmospheric scattering models(ASM), existing haze removal methods suffer from some drawbacks, such as imbalanced contrast and missing details. To address the above issues, this paper proposes a two-stage dehazing network called CEDhazeNet. It consists of a contrast enhancement module (CE) and a texture detail restoration module (TDR). In CE, we introduce a novel curve enhancement dehazing model to tackle the problem of contrast imbalance. Specifically, we observe that inverting the hazy image can accentuate all the regions obscured by haze, and by enhancing the exposure of the inverted image, we can significantly reduce the impact of haze on image contrast. In TDR, we construct a multi-scale information distillation network, which use more effective information, such as edges and spots to recover the texture details of hazy image. Extensive experiments on both synthetic and real world hazy image datasets demonstrate that CEDhazeNet outperforms state-of-the-art haze removal methods in terms of quantitative accuracy and subjective visual quality.

关键词： image Dehazing Curve-enhanced Model Inverted image

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LIGHTING image/VIDEO STYLE TRANSFER METHODS BY ITERATIVE CHANNEL PRUNING 49

LIGHTING IMAGE/VIDEO STYLE TRANSFER METHODS BY ITERATIVE CHA...

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49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wu, Kexin Tang, Fan Liu, Ning Deussen, Oliver Le, Thi-Ngoc-Hanh Dong, Weiming Lee, Tong-Yee Jilin Univ Jilin Jilin Peoples R China ICT CAS Beijing Peoples R China Midea Grp Beijiaozhen Peoples R China Univ Konstanz Constance Germany Natl Cheng Kung Univ Tainan Taiwan CASIA Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deploying style transfer methods on resource-constrained devices is challenging, which limits their real-world applicability. To tackle this issue, we propose using pruning techniques to accelerate various visual style transfer methods. We argue that typical pruning methods may not be well-suited for style transfer methods and present an iterative correlation-based channel pruning (ICCP) strategy for encoder-transform-decoder-based image/video style transfer models. The correlation-based channel regularization preserves the feature distributions for content and style references, and the iterative pruning strategy prevents layer collapse when pruning on the encoder-decoder structure. Experiments demonstrate that the proposed ICCP can generate visual competitive results compared to SOTA style transfer methods and significantly reduces the number of parameters (at least 70K) and inference time. Model is available at https://***/wukx-wukx/ICCP.

关键词： visual style transfer model pruning

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Enhancing image Quality through Fractional Order Unsharp Masking with Particle Swarm Optimization 2

Enhancing Image Quality through Fractional Order Unsharp Mas...

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2nd ieee International conference on Networking and communications, ICNWC 2024

作者： Gamini, Sridevi Kamisetti, Kavya Sri Nallamilli, Priyanka Darisi, Sai Pavan Venkatapathi, Venkatesh Aditya Engineering College Dept. of ECE Surampalem India

ISBN: (纸本)9798350365269

This paper presents a novel approach to image enhancement using Fractional-Order Unsharp Masking (FOUM) combined with Particle Swarm Optimization (PSO). The proposed method aims to improve the quality of digital images by enhancing their contrast and details. The PSO is employed to optimize the parameters involved in the FOUM process which includes the fractional order, and the weights for combining Laplacian and fractional differential filter. In this approach, Grunwald-Letnikov fractional differential filter is employed. Experimental results demonstrate that the proposed method outperforms traditional image enhancement techniques in terms of entropy and average values, yielding images with enhanced visual quality and improved feature preservation. © 2024 ieee.

关键词： image enhancement

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