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Improving the spatial resolution of digital images and video sequences using subpixel scanning

Improving the spatial resolution of digital images and video...

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2021 All-Russian conference with international Participation "Spatial Data processing for Monitoring of Natural and Anthropogenic Processes", SDM 2021

作者： Reznik, Aleksandr L. Soloviev, Aleksandr A. Torgov, Andrey V. Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia

High-performance method for improving the resolution of digital images and video sequences based on minimum-variance signal reconstruction are considered. A distinctive feature of the developed algorithms is that they allow (with the availability of modern computing power) to obtain improved images and video in "real time". © 2021 Copyright for this paper by its authors.

关键词： High-performance algorithms image reconstruction

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A Survey of image Classification algorithms Based on Graph Neural Networks 2nd

A Survey of Image Classification Algorithms Based on Graph N...

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2nd international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSPandDL 2020

作者： Liu, Chan Cheng, Feiyan School of Information Yunnan Normal University Kunming China

ISBN: (纸本)9789811633904

image classification is an image processing method which can distinguish different objects according to their different features reflected in the image information. A graph neural network (GNN) is a connectivity model that captures graph dependencies through messaging between nodes of a graph. After a systematic study of the graph neural network and its application in image processing, this paper presents the mainstream structure model for image classification based on the graph neural network, and synthetically compares the performance of the more popular graph neural network model by analyzing the application scenarios and solving problems of the model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zero-shot learning

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image CAPTION GENERATOR USING ATTENTION MECHANISM 12

IMAGE CAPTION GENERATOR USING ATTENTION MECHANISM

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12th international conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Agrawal, Vaishnavi Dhekane, Shariva Tuniya, Neha Vyas, Vibha Electronics and Telecommunication Engineering College of Engineering Pune Pune India

ISBN: (纸本)9781728185958

image captioning is used to generate sentences describing the scene captured in the form of images. It identifies objects in the image, performs a few operations, and tries to find the salient features of the image. Once the system identifies this information, it should further generate the most relevant and brief description for the image, which should be both syntactically and semantically correct. With the advancements of Learning techniques, algorithms can generate text in the form of natural sentences that will be able to describe an image in its best form. The natural ability of humans to understand image content and generate descriptive text is a challenging task for a machine to imitate. The applications of image caption generation are extensive and significant. The task involves generating brief captions using various techniques like Natural language processing (NLP), Computer vision (CV), and Deep Learning (DL) techniques. This paper introduces a system that uses an attention mechanism alongside an encoder and a decoder to generate the captions. It uses a pre-trained Convolutional Neural Network (CNN) viz. Inception V3 to extract features of the image and then a Recurrent Neural Network (RNN) viz. GRU to generate a relevant caption. To generate captions, the proposed model uses an attention mechanism that is Bahdanau attention. MSCOCO dataset is used to train the model. The results validate the model's reasonable ability to understand images and generate text. © 2021 IEEE.

关键词： Computer vision

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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： image sensors Training Computer vision Pipelines Object detection Computer architecture Dynamic range

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international Youth conference on Electronics, Telecommunications, and Information Technologies, YETI 2021

International Youth Conference on Electronics, Telecommunica...

引用

international Youth conference on Electronics, Telecommunications, and Information Technologies, YETI 2021

ISBN: (纸本)9783030811181

The proceedings contain 64 papers. The special focus in this conference is on Electronics, Telecommunications, and Information Technologies. The topics include: Application of Deep Learning Techniques for Detection of COVID-19 Using Lung CT Scans: Model Development and Validation;preface;synthesis and Analysis of Avionics Functions Digital Twins Using Machine Learning Classification algorithms;algorithm for the Joint Analysis of Beat-To-Beat Arterial Pressure and Stroke Volume for Studying Systemic Vasoconstrictor and Vasodilator Responses;development of Simple Model of the Arterial Baroreflex;Analysis of Classification Methods for Wi-Fi signals Based on the Use of Channel State Information Spatial Features and CNN;application of Top-Down Deconvolution Tools to Bottom-Up Mass Spectrometry Data: Advantages and Limitations;algorithm for Fetal Activity Passive Monitoring;lossless Genome Data Compression Using V-Gram;The Method of Generating Random OQPSK signal Packets Based on Optimal FTN Pulses with Reduced PAPR and Frequency Band;iterative Algorithm with Feedback for Coherent Detection of Non-orthogonal Spectrally Efficient Multi-frequency signals;development of a Universal System for Three-Dimensional Data Visualization;Extracting Human Brain signals from the EEG Records Using LabVIEW and advanced signal processing;on the Application of a Fully Connected Neural Network for Multifrequency signals Demodulation;identification of Mortality Predictors for Elderly Patients with Airway Cancer;FPGA Implementation of Outer Decoders for Sequential Decoder of Polar Codes;integrated Digital Voltage Regulator with Phase-Frequency Control for Wireless Passive Microdevices;Aerosol Transport Modeling for Evaporator of CVD Reactor;Numerical Simulation of CVD Reactor for Oxide Semiconductor Layer Deposition;design of GaAs Flip-Flop Using Depletion-Mode-Only Transistors;comparative Study of Ion-Induced Damage Formation in GaN and beta-Ga2O3.

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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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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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Behavioural Study of Constrained Convolutional Neural Network on image Splicing Classification of Various Datasets 11th

Behavioural Study of Constrained Convolutional Neural Networ...

引用

11th international conference on Robotics, Vision, signal processing and Power Applications, RoViSP 2021

作者： Lee, Yang Yang Kong, Teck Seng Khoo, Bee Ee Universiti Sains Malaysia Penang Nibong Tebal14300 Malaysia

ISBN: (纸本)9789811681288

The Internet nowadays is exploded with digital images. However, it also has more forged images floating around and threatening the digital asset trustworthy, especially spliced images. Although there are many computational ways of detecting image splicing, the image splicing technique is advancing over time, driving feature specific algorithms to obsolete. Thus, more researchers nowadays are focusing on data-driven feature extraction. Convolutional Neural Network (CNN) is a Deep Learning algorithm well known for object detection, but it is ill-suited for manipulation feature extraction. In this research, a constrained convolution algorithm is to be injected into a simple CNN to study its performance in image splicing detection in a wide range of datasets. A strategic parameter tweaking can fit a wide range of spliced image datasets, but it is always biased to its train dataset. A cross-database splicing classification shows that it is ill-suited for generalizing a wide range of dataset evaluation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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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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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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