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image Compressed Sensing Reconstruction via Deep image Prior with Feature Space and Texture Information 8th

Image Compressed Sensing Reconstruction via Deep Image Prior...

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8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Peng, Zhao Jinchan, Wang Huanqing, Peng Fei, Xiang Liwen, Zhang College of Information Engineering Henan University of Science and Technology Luoyang471023 China

ISBN: (纸本)9789819980178

Deep learning has yielded remarkable achievements in compressed sensing image reconstruction in recent years. However, when the sampling rate is low, the reconstruction image is blurred and lacks texture details. In this paper, we design a dual-path compressed sensing reconstruction network based on image feature space information flow and texture information, which can solve the above problems to a certain extent. The feature space information flow path employs proximal gradient descent algorithms to map essential structural information from the pixel space to the feature space, effectively reducing data dimensionality and redundancy. On the other hand, after combining the attention mechanism, the texture information path restores the texture details of the image in the frequency domain, which can effectively enhance the quality and visual effects of the reconstruction image. During training, a unified loss function guides the alternating optimization of both paths. Evaluation of prominent benchmark datasets, including Set5, Set11, and BSD68, reveals that our proposed method outperforms traditional iterative approaches and existing deep learning-based methodologies in terms of both reconstructed image quality and network robustness under low sampling rates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Mining Regional Relation from Pixel-wise Annotation for Scene Parsing

Mining Regional Relation from Pixel-wise Annotation for Scen...

引用

IEEE visual communications and image processing (VCIP)

作者： Zichen Song Hongliang Li Heqian Qiu Xiaoliang Zhang University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665475938

Scene parsing is an important and challenging task in computer vision, which assigns semantic labels to each pixel in the entire scene. Existing scene parsing methods only utilize pixel-wise annotation as the supervision of neural network, thus, some similar categories are easy to be misclassified in the complex scenes without the utilization of regional relation. To tackle these above challenging problems, a Regional Relation Network (RRNet) is proposed in this paper, which aims to boost the scene parsing performance by mining regional relation from pixel-wise annotation. Specifically, the pixel-wise annotation is divided into a lot of fixed regions, so that intra- and inter-regional relation are able to be extracted as the supervision of network. We firstly design an intra-regional relation module to predict category distribution in each fixed region, which is helpful for reducing the misclassification phenomenon in regions. Secondly, an inter-regional relation module is proposed to learn the relationships among each region in scene images. With the guideline of relation information extracted from the ground truth, the network is able to learn more discriminative relation representations. To validate our proposed model, we conduct experiments on three typical datasets, including NYU-depth-v2, PASCAL-Context and ADE20k. The achieved competitive results on all three datasets demonstrate the effectiveness of our method.

关键词： image segmentation Computer vision Annotations visual communication Semantics Neural networks Data mining

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Drone-Based Car Counting via Density Map Learning

Drone-Based Car Counting via Density Map Learning

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Huang, Jingxian Ding, Guanchen Guo, Yujia Yang, Daiqin Wang, Sihan Wang, Tao Zhang, Yunfei Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Inc Shenzhen 518052 Peoples R China

ISBN: (纸本)9781728180687

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

关键词： Car counting density map learning drone-based image

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Deep learning-based Object Detection in Underwater communications System 2

Deep learning-based Object Detection in Underwater Communica...

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Sangari, M. Siva Thangaraj, K. Vanitha, U. Srikanth, N. Sathyamoorthy, J. Renu, K. KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sona College of Technology Department of Information Technology Salem India Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India SASI Institute of Technology and Engineering Department of Electronics and Communication Technology Andhra Pradesh Tadepalligudem India Bannari Amman Institute of Technology Department of CSE Erode India GST Department of EECE GITAM Deemed to Be University A.P Visakhapatnam -45 India

ISBN: (纸本)9798350397635

Being at the nexus of robotics and ocean engineering, underwater robots have been a developing research area. They can be used for deep sea infrastructure inspections, oceanographic mapping, and environmental monitoring. Autonomous navigation skills are essential for doing these activities successfully, especially given the poor communication conditions in underwater locations. Autonomous navigation technologies, such as path planning and tracking, have been one of the fascinating but difficult issues in the field of study due to the extremely dynamic and three-dimensional settings. Due to their short detection ranges and poor visibility, cameras have not received much attention as an underwater sensor. However, using visual data from cameras is still a popular technique for underwater sensing, and it works particularly well for close-range detections. In this study, the enhancement of underwater vision is achieved by combining the max-RGB and shades of grey methods. Then, to solve the problem of poorly illuminated underwater images, a technique known as RCNN (Region-based Convolutional Neural Network) is proposed. This procedure tells the mapping relationship how to create the illumination map. Following image processing, an RCNN strategy for underwater detection and classification is recommended. Two improved strategies are then used to change the RCNN structure in accordance with the properties of underwater vision. In order to deal with the challenges of object tracking and detection in underwater communication, a correlation filter tracking algorithm (CFTA) method was created. The properties of the invariant moment and area were looked at after the object's region had been extracted using a threshold segment and morphological technique. The findings show that the suggested method is effective for underwater target tracking based on RCNN-CFTA in the aquatic environment. Simulated evaluation of these methods' performance demonstrates the potency of the suggested

关键词： Cameras

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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Untargeted Attack on Targeted-label for Multi-label image Classification 12

Untargeted Attack on Targeted-label for Multi-label Image Cl...

引用

12th International conference on Graphics and image processing (ICGIP)

作者： Lin, Yangfei Qiao, Peng Dou, Yong Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781510642782;9781510642775

Multi-label image Classification (MLIC) is widely used in scene understanding, multi-object recognition, visual question answering and so on. Although providing promising performance, CNN-based models have been shown to be highly vulnerable to adversarial examples. Besides the targeted and untargeted attacks, which are introduced in Single-label image Classification, there is a new type of attack on MLIC, i.e., the untargeted attack on targeted-label. In the untargeted attack on targeted-label for MLIC, one tries to interfere with the prediction of CNNs, causing untargeted attack on a targeted-label. To achieve this attack, we propose to reduce the spatial range of the adversarial attack from the whole image to where the object of the targeted-label is most likely to appear. We conducted the untargeted attack on targeted-label for MLIC in both PASCAL VOC 2007 and MS-COCO 2014 dataset. Experimental results indicate that the proposed method is effective to undermine CNN-based models for MLIC. The success rate of the untargeted attack on targeted-label using the proposed mask increases about 10%. In VOC2007, the success rate increase from 67.9% to 75.7%. In COCO2014, the success rate increases from 55.7% to 66.9%.

关键词： multi-label image classification neural networks adversarial attack

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A Fine-Grained image Description Generation Method Based on Joint Objectives 18th

A Fine-Grained Image Description Generation Method Based on...

引用

18th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Zhang, Yifan Lin, Chunzhen Cao, Donglin Lin, Dazhen Artificial Intelligence Department Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China

ISBN: (纸本)9789819996360

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model’s performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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Underwater image Restoration Based on An Improved Underwater image Formation Model

Underwater Image Restoration Based on An Improved Underwater...

引用

International conference on Wireless communications and Signal processing (WCSP)

作者： XinNan Fan Xuan Zhou PengFei Shi HuanRu Sun ShuYue Ye College of Internet of Things Engineering Hohai University Changzhou China

ISBN: (纸本)9781665450867

Underwater images often show characteristics of low contrast, low visibility and color distortion. To improve underwater image quality, a novel underwater image restoration algorithm is proposed in this paper. First of all, we put forward an underwater image restoration network (UIRnet) based on CNN(Convolutional Neural Network) for transmission estimation in the underwater imaging model. Besides, the Leaky ReLU(Rectified Linear Units) activation function and SENet(Squeeze-and-Excitation Networks) unit are used to improve the ability of extracting image features and training parameters. Secondly, the fuzzy patch dataset which is used as input dataset is synthesized by the underwater optical imaging model and randomly generated transmission. Meanwhile, the corresponding transmission value is used as label dataset. Then, we use the dataset to train and optimize all parameters in the UIRnet. Finally, the trained UIRnet is used in underwater image restoration. The experimental results show that the proposed algorithm can restore the details of the image, improve the contrast, and have a more natural visual effect.

关键词： Training Wireless communication image quality Neural networks Signal processing algorithms Network architecture Feature extraction

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Transfer learning based multi-class malware classification using VGG16 feature extractor and SVM classifier

Transfer learning based multi-class malware classification u...

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International conference on communications and Signal processing

作者： Harman Saggu Er. Karandeep Singh T. N. Suresh Babu Department of Computer Science and Engineering Punjabi University Patiala India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Malicious programs are becoming a severe problem in today’s internet-connected world. Several works have explored image analysis-based malware detection in recent years. The image analysis method converts the malicious code into gray-scale images, which enables the use of deep learning models in the domain of malware detection. This paper proposes a new architecture using a fine-tuned visual Geometry Group(VGG16) model with a support vector machine (SVM) classifier to achieve high-performance accuracy in detecting and classifying evolving malware. At first, the malicious code is represented in gray-scale images, which are further reshaped and resized to be fed into a fine-tuned VGG16 model as end-to-end feature extraction with the least dependence on human-crafted feature engineering. The extracted textural feature maps are further correlated to remove the highly correlated features before using them to train a support vector machine (SVM) classifier. The experiments are performed on public bench-marked MalImg datasets and actual malware samples. The VGG 16+ SVM model obtained 99.25% classification accuracy for the MalImg and 80.29% for real-world malware, which signifies that the model can detect real-world instances of malware. The proposed method outperforms similar existing works and is suitable for deployment in an IoT environment due to the least cost involved in feature engineering.

关键词： Support vector machines Geometry Deep learning visualization image analysis Costs Transfer learning

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High-frequency guided CNN for video compression artifacts reduction

High-frequency guided CNN for video compression artifacts re...

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IEEE visual communications and image processing (VCIP)

作者： Li Yu Wenshuai Chang Qingshan Liu Moncef Gabbouj School of Computer Science Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Department of Computing Sciences Tampere University Tampere Finland

ISBN: (纸本)9781665475938

In this paper, we propose a high-frequency guided CNN for video compression artifacts reduction. In the proposed method, high frequency component in Y channel is extracted and used to guide the quality enhancement of all Y, U, V channels. As high frequency component contains the edge and contour information of the objects in the image, which is of vital importance to both subjective and objective quality. In general, the proposed method consists of two modules: the high frequency guidance module and the quality enhancement module. The high-frequency guidance module uses multiple octave convolutions to extract the high-frequency component in Y channel and then fuse it into the features of Y, U, and V channels. While in the quality enhancement module, multiple CNN residual blocks are used for the quality enhancement of Y, U, and V channels. The proposed method was integrated into both HM-16.22 and VTM-16.0. The results on the JVET test sequence under All Intra configuration shows the effectiveness of the proposed method. Compared with HEVC, the proposed method achieves the average BD-rate reductions of -12.3%, -22.7% and -23.5% for Y, U and V channels respectively. Compared with VVC, the average BD-rate reductions are -6.7%, -12.3% and -13.2% correspondingly.

关键词： visual communication Fuses image edge detection Video compression Feature extraction High frequency

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