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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是261-270 订阅

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DeCloud GAN: An advanced Generative Adversarial Network for Removing Cloud Cover in Optical Remote Sensing imagery 4

DeCloud GAN: An Advanced Generative Adversarial Network for ...

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4th international conference on Computational Intelligence and Intelligent Systems (CIIS)

作者： Bhambani, Krisha Takalikar, Mukta Pune Inst Comp Technol Pune Maharashtra India

ISBN: (纸本)9781450385930

Optical Remote Sensing imagery has several applications in monitoring the states of natural and man-made features around the globe. However, due to clouds and other climatic conditions, information extracted from the imagery retrieved is very limited. Deep learning has often been used in several image processing and remote sensing tasks. In this work, we propose the usage of generative adversarial networks to remove clouds and other climatic interference from high-resolution remote sensing imagery. We have trained and tested upon the Remote sensing image Cloud rEmoving dataset (RICE). The novel network(DeCloud GAN) we propose, makes use of residual UNets and pixel shuffle layers in the generator, which yield high quality cloudless satellite images. We have tested 4 methods for comparison, and have found that DeCloudGAN achieves the best performance on two main metrics, peak signal to noise ratio (PSNR) and structural similarity index (SSIM), to measure similarity in visual perception of the produced and target images.

关键词： Generative Adversarial Networks Deep Learning Optical imagery Remote Sensing image generation

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Proceedings of 2021 international conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021) 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Ruidan Su Yu-Dong Zhang Han Liu

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New Generative image Model for Variational Autoencoders Based on Counts Partition 7

New Generative Image Model for Variational Autoencoders Base...

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7th IEEE international conference on Information Technology and Nanotechnology, ITNT 2021

作者： Antsiperov, Viacheslav Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences Signal Processing and Telecommunication Lab Moscow Russia

ISBN: (纸本)9781665432177

The article substantiates a generative image model based on a sample of random counts and its representation using compressed (coded) lattice partitioning. The basics of the model proposed is a concept of the ideal image. To avoid the problems with cumbersome descriptions, the ideal image is reduced to a sample of counts of a fixed (controlled) size. It is shown that the corresponding statistical description can be factorized into the product of the distributions of individual counts, which fits well with naive Bayesian algorithms and other machine learning approaches. Guided by this association in the image and likeness of the well-known K-means algorithm, the method of iterative partitioning - maximization of counts was synthesized. The main goal of the method is to calculate the maximum likelihood parameters of sample probability distribution and to use these parameters as a code for some intermediate representation of the input data. The method specific feature is the use of lattice neighbourhood trick for partitioning counts, that allows to control both the computation time and some characteristics of the resulting code. The final of the article is devoted to the algorithmic details of the proposed partition-maximization method are considered and several iterations related characteristics, including the convergence property, convergence criterion and asymptotic efficiency of the resulting estimates are discussed. Most of those characteristics are illustrated by the results of numerical simulations. © 2021 IEEE.

关键词： K-means clustering

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Proceedings of the 2021 1st international conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

Proceedings of the 2021 1st International Conference on Adva...

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1st IEEE international conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

ISBN: (纸本)9781728157900

The proceedings contain 199 papers. The topics discussed include: evaluating the effectiveness of community detection algorithms for influence maximization in social networks;design and fabrication of Bio-MEMS device for detection of heavy metalion present in potable water;towards prediction of academic school of students for real-time;advanced analysis of cardiac image processing using hybrid approach;a comparative performance analysis of stellar SDN controllers using emulators;an improved image encryption using 2D logistic adjusted sine chaotic map with shuffled index matrix;substituent effect on the chalcones and their DFT studies;and bidirectional DC-DC converter fed BLDC motor in electric vehicle.

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Lightweight Multi-scale Attentional Network for Single image Dehazing

Lightweight Multi-scale Attentional Network for Single Image...

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international conference on image processing, Computer Vision and Machine Learning (ICICML)

作者： Zong, Ping Li, Jinjiang Hua, Zhen Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai Peoples R China

ISBN: (纸本)9781665464680

Neural network-based image dehazing algorithms have obtained extensive research results. However, haze retention is still present in the results for complex outdoor images. Designing complicated networks may be improved, yet they consist of a tremendous number of parameters, which causes slow inference and requires a more demanding computer environment for *** network globally adopts a U-Net-like structure and employs two improved modules ARM and SAM based on the attention mechanism to implement multi-scale feature extraction and fusion. Our proposed network combines the high and low level semantics in diverse scale feature images while raising the focus on diverse haze concentration regions. Experimental results reveal that our network can get improved visual results in RESIDE-SOTS (Outdoor) and NTIRE2021 NH-HAZE image Dehazing Challenge datasets in comparison with other SOTA methods and attains higher Peak signal to Noise Ratio and Structural Similarity Index.

关键词： image dehazing multi-scale feature extraction and fusion

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Rapid Automatic Underwater image Recovery Method Based on Polarimetric Imaging

Rapid Automatic Underwater Image Recovery Method Based on Po...

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2021 international conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and signal processing Technology

作者： Wang, Hongyuan Hu, Haofeng Jiang, Junfeng Xu, Jianuo Liu, Hedong Liu, Tiegen School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin300072 China Ministry of Education Tianjin300072 China School of Marine Science and Technology Tianjin University Tianjin300072 China

ISBN: (纸本)9781510655676

Underwater imaging plays an important role in underwater scientific research, biological monitoring, underwater rescue, and military defense. However, the scattering and absorption of particles in the scattering medium lead to the distortion of the originally ordered wavefront phase and the image degradation. Polarimetric imaging technology has become an effective method for underwater image recovery based on its unique advantages. However, traditional polarimetric techniques require mechanically rotating the linear polarizer to find the two images that are brightest (the backscattered light is maximally through the polarizer) and darkest (the backscattered light is maximally blocked by the polarizer). Most polarimetric imaging techniques combined with conventional methods require the presence of a background region in the image to estimate the characteristics of backscattered light. There are also some polarimetric imaging methods that do not require a background area in the image, but most of them require prior knowledge and complex algorithms. All these greatly affect the real-time application of polarimetric imaging technology. In this paper, a rapid automatic underwater image recovery method based on polarimetric imaging is proposed. We optimize the traditional polarimetric imaging method based on the scattering imaging model and solve the problem that the traditional method requires an unsupervised image quality evaluation index to correct the intermediate parameters and selecting the background region, which realize the scattering suppression for the underwater image without background region. The experimental results show that our method can process a 320,000-pixel image in only 0.007 seconds. The proposed method can be applied to the current polarimetric imaging instrument system and has a good effect on underwater image recovery, and the recovery results are almost without distortion. It has strong robustness and is also applicable to images without backgr

关键词： Underwater imaging

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Reversible Data Hiding in Color image with Grayscale Invariance using Sorting

Reversible Data Hiding in Color Image with Grayscale Invaria...

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2021 IEEE international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2021

作者： Yuan, Junying Wu, Min-Hao Chang, Ting-Cheng Zhan, Zengrong Chen, Haishan School of Electrical and Computer Engineering Nafang College • Guangzhou Guangzhou China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou China

ISBN: (纸本)9780738145334

Most reversible data hiding (RDH) algorithms for color images directly embed secret information in the luminance channel. As a result, the grayscale version of the color image is usually distorted. Since many color image processing algorithms work on the grayscale version, their performance on marked color images may get seriously impacted. Therefore, RD H with grayscale invariance (RDH-GI) is advanced to eliminate such shortcomings and has received great attention thereafter. However, their performance is still far from satisfactory. This paper presents a novel RDH-GI method by exploiting the pixel clustering technique to segment host pixels into different complexity levels. Data embedding tends to take place in less complex regions, and thus image distortion is reduced while maintaining grayscale invariance. Experiments prove the effectiveness of the proposed work. © 2021 IEEE.

关键词： Pixels

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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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Sparse noise minimization in image classification using Genetic Algorithm and DenseNet 5

Sparse noise minimization in image classification using Gene...

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5th IEEE Annual international conference on Information Communications Technology and Society (ICTAS)

作者： Mienye, Ibomoiye Domor Ainah, Priye Kenneth Emmanuel, Ikiomoye Douglas Esenogho, Ebenezer Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa Niger Delta Univ Dept Elect & Elect Engn Wilberforce Isl Nigeria Univ Salford Sch Comp Sci & Engn Salford Lancs England Univ Johannesburg Ctr Telecommun Dept Elect & Elect Engn Johannesburg South Africa

ISBN: (纸本)9781728180816

Noise handling is a critical aspect of image processing, which can significantly affect the accuracy of classification and recognition algorithms. In this paper, we propose a technique for improved noise handling in sparse input feature maps where the noise signal is also sparse. The signal-noise relationship is formulated as an optimization problem which is solved by a genetic algorithm. The genetic algorithm is applied to optimize the setting of a non-convexity parameter which yields a more accurate image sparse matrix. The resulting feature map is then classified using a densely connected convolutional network (DenseNet). Lung computed tomography images were used for the experiments. The proposed approach achieves better performance when the classification results are compared with a case in which the input signal has not been denoised using the proposed approach.

关键词： ADMM DenseNet genetic algorithm lung cancer sparse noise

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RW-HAZE: A REAL-WORLD BENCHMARK DATASET TO EVALUATE QUANTITATIVELY DEHAZING algorithms 29

RW-HAZE: A REAL-WORLD BENCHMARK DATASET TO EVALUATE QUANTITA...

引用

IEEE international conference on image processing (ICIP)

作者： Chen, Jiyou Wang, Shengchun Liu, Xin Yang, Gaobo Hunan Univ Sch Comp Sci & Elect Engn Changsha 410082 Peoples R China Hengyang Normal Univ Sch Phys & Elect Engn Hengyang 421008 Peoples R China Hunan Normal Univ Sch Informat Sci & Engn Changsha 410081 Peoples R China Hunan Appl Technol Univ Changde 415100 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Most existing image dehazing approaches are able to achieve desirable results whose differences are too subtle for people to qualitatively judge. Therefore, it is important to adopt quantitative assessment on real-world hazy images. However, many dehazing works have not been quantitatively evaluated on real-world hazy images due to the lack of appropriate real-world datasets. In this work, we attempt to address the issue and present a well-aligned real-world benchmark dataset, namely RW-Haze, for image dehazing evaluation, which had been lacking for a long period of time. It contains 210 pairs of well-aligned haze-free images and hazy images with distinct haze densities, which were captured from six cities in China by fixed cameras. To the best of our knowledge, RW-Haze is the first real-world dataset that is made up of well-aligned image pairs of haze-free and hazy images with diverse haze levels. We select 13 state-of-the-art single image dehazing works for making comprehensive evaluations among them on RW-Haze dataset. Experimental results show that there still exist rich rooms for image dehazing research to improve its robustness on natural hazy images, especially dense haze scenes.

关键词： image dehazing benchmark dataset performance evaluation

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