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A Study on the Impact of Training Data in CNN-Based Super-Resolution for Low Bitrate End-to-End Video Coding

A Study on the Impact of Training Data in CNN-Based Super-Re...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Fatemeh Nasiri Wassim Hamidouche Luce Morin Gildas Cocherel Nicolas Dhollande IRT b<>com Cesson-Sévigné France Univ Rennes INSA Rennes CNRS IETR - UMR 6164 Rennes France AVIWEST Saint-Gregoire France

ISBN: (数字)9781728187501

ISBN: (纸本)9781728187518

In this study, the effectiveness of Super Resolution (SR) methods based on Convolutional Neural Network (CNN) in low bitrate video coding, with a focus on the Versatile Video Coding Standard (VVC), is investigated. Video transmission over networks with limited bandwidth is a common challenge for different applications. One solution is to adopt SR methods where the main principle is to spatially down-sample the input sequence prior to the encoding, then up-sampling the decoded sequence before displaying it. For a fixed target bandwidth, a finer quantization is applied on the low-resolution sequence compared to high-resolution, so that the high quality reconstructed pixels help in retrieving the lost information. However, most CNN-based SR methods are designed for single images and merely focus on the original input signal. Therefore, their trained networks lack understanding of compression artifacts. In this study, we test a hypothesis that training CNN-based SR methods with compressed sequences outperforms training with uncompressed ones. The assumption is that such training allows the SR methods to learn compression artifacts and differentiate them from actual texture information. To this end, state-of-the-art CNN-based SR methods are tested with compressed and uncompressed training set. Experiments show that the use of compressed training data brings, on average, an additional bitrate saving of 6%, in terms of BD-Rate.

关键词： Training Video coding image coding Bit rate Training data Bandwidth Convolutional neural networks

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Three-dimensional Imaging Sonar signal processing System Based on Blade Server 2

Three-dimensional Imaging Sonar Signal Processing System Bas...

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2019 2nd International conference on Advanced Algorithms and Control Engineering, ICAACE 2019

作者： Zeng, xiangrui Liu, Jingyang Gao, Bo Science and Technology on Sonar Laboratory Hangzhou Research Institute of Applied Acoustics Hangzhou310012 China

The three-dimensional high-resolution imaging sonar uses multi-beam technology combined with synthetic aperture technology to achieve centimeter-level imaging of small underwater targets. It has a good imaging effect on underwater anchoring, sinking mine and buried mine target detection and underwater topography, and has a wide range of military and civil engineering applications. The system has a large amount of data transmission and computation, including multiple functional modules such as synthetic aperture, high resolution array processing, image processing, and target recognition. The software architecture is complex and real-time system development faces serious challenges. The blade servers have been widely used in the telecommunications, financial and other big data processing fields as for the high efficiency, stability and autonomy. This paper introduces a cluster 3D imaging signal processing system based on x240m5 blade server. The message queue communication (ZMQ) is used to realize the stable transmission of large data volume. At the same time, the mixed programming technology of C language and Matlab language combines the convenience of real-time and software integration, which greatly shortens the development cycle. The system runs stably and realizes the imaging recognition of the small spheres and their clump weights in the simulated target water. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic apertures

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image FUSION USING BELIEF PROPAGATION

IMAGE FUSION USING BELIEF PROPAGATION

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IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hill, Paul Bull, David Univ Bristol Dept Elect & Elect Engn Bristol BS8 1UB Avon England

ISBN: (纸本)9781538646588

This paper describes the application of belief propagation methods to image fusion within a complex wavelet decomposition (the Dual Tree Complex Wavelet Transform: DT-CWT). Belief propagation within each transform subband iterates through a lattice based Bayesian belief network. This leads to precisely controlled spatial coherence of subband coefficient fusion through the definition of belief graph probabilities. This results in a significant improvement in quantitatively measured fusion performance for a large database of over 160 fusion image pairs from a range of fusion applications including remote sensing, multi-focus and multi-modal sources. Improvements in qualitative image fusion performance is also demonstrated.

关键词： image Fusion wavelets Bayesian Belief Networks Bayesian Belief Propagation

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Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Algorithms and Fundamental Limits for Unlabeled Detection Us...

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作者： Marano, Stefano Willett, Peter K. Dipartimento di Ingegneria dell'Informazione Ed Elettricae Matematica Applicata University of Salerno FiscianoI-84084 Italy Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269-2157 United States

We deal with the classical problem of testing two simple statistical hypotheses but, as a new element, it is assumed that the data vector is observed after an unknown permutation of its entries. What is the fundamental limit for the detection performance in this case? How much information for detection is contained in the entry values and how much in their positions? In the first part of this paper, we answer these questions. In the second part, we focus on practical algorithms. A low-complexity detector solves the detection problem without attempting to estimate the permutation. A modified version of the auction algorithm is then considered, and two greedy algorithms with affordable worst case complexity are presented. The detection operational characteristics of these detectors are investigated by computer experiments. The problem we address is referred to as unlabeled detection and is motivated by large sensor network applications, but applications are also foreseen in different fields, including image processing, social sensing, genome research, and molecular communication. © 1991-2012 IEEE.

关键词： image processing

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LEARNING TRANSMISSION FILTERING NETWORK FOR image-BASED PM2.5 ESTIMATION

LEARNING TRANSMISSION FILTERING NETWORK FOR IMAGE-BASED PM2....

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IEEE International conference on Multimedia and Expo (ICME)

作者： Liao, Yinghong Qiu, Bin Su, Zhuo Wang, Ruomei He, xiangjian Sun Yat Sen Univ Sch Data & Comp Sci Natl Engn Res Ctr Digital Life Guangzhou Guangdong Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou Fujian Peoples R China Univ Technol Sydney Sch Comp & Commun Sydney NSW Australia

ISBN: (纸本)9781538695524

PM2.5 is an important indicator of the severity of air pollution and its level can be predicted through hazy photographs caused by its degradation. image-based PM2.5 estimation is thus extensively employed in various multimedia applications but is challenging because of its ill-posed property. In this paper, we convert it to the problem of estimating the PM2.5-relevant haze transmission and propose a learning model called the transmission filtering network. Different from most methods that generate a transmission map directly from a hazy image, our model takes the coarse transmission map derived from the dark channel prior as the input. To obtain a transmission map that satisfies the local smoothness constraint without regional boundary degradation, our model performs the edge-preserving smoothing filtering as the refinement on the map. Moreover, we introduce the attention mechanism to the network architecture for more efficient feature extraction and smoothing effects in the transmission estimation. Experimental results prove that our model performs favorably against the state-of-the-art dehazing methods in a variety of hazy scenes.

关键词： PM2.5 estimation edge-preserving smoothing image dehazing deep networks

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Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator

Deep Face Recognizer Privacy Attack: Model Inversion Initial...

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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Mahdi Khosravy Kazuaki Nakamura Naoko Nitta Noboru Babaguchi Media Integrated Communication Laboratory Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

A variety of Machine Learning (ML) applications involve data of privacy-sensitive content. Face recognizer is one of them which due to training by user identities face images, it is inherent to user face image as a critical biometric data. A face recognition system can be subject to privacy attacks even though it deploys a complex model structure like a deeplearning-based one. Because as the ML models advance to carry more complexity in structure and parameters, the privacy attack trends develop too. Model Inversion Attack (MIA) pioneered by Fredrikson et al [1] was applied on a shallow neural network of face recognizer, and its capability of privacy leakage was approved. Their work was on a white-box scenario wherein besides the model structure, the privacy-non-sensitive data of the users are partially available and used by the attacker for generation and leakage of the user identity face images. The present work improves the extension of MIA to deep learning models of face recognizers while performing without any data of the users. Despite the complexity of the deep models as an obstacle, this work improves the capability of MIA in this matter. To aim this goal, it initializes its training procedure by a seed image approved by a GAN-trained discriminator of face image data-space via its output probability value. In targeting two users' identities by MIA, the proposed technique approves its efficiency on a deep face recognition system. The recognition rates of the images generated by MIA associated with GAN data-space discriminator (GAN-DD) are higher than sole MIA and demonstrate efficiency improvement of deep MIA.

关键词： Face recognition Data models Training image recognition Generative adversarial networks Gallium nitride Deep learning

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Rendering-dependent compression and quality evaluation for light field contents 42

Rendering-dependent compression and quality evaluation for l...

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conference on applications of Digital image processing xLII

作者： Viola, Irene Takahashi, Keita Fujii, Toshiaki Ebrahimi, Touradj Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp MMSPG Lausanne Switzerland Nagoya Univ Sch Engn Nagoya Aichi Japan

ISBN: (数字)9781510629684

ISBN: (纸本)9781510629684

Light field rendering promises to overcome the limitations of stereoscopic representation by allowing for a more seamless transition between multiple point of views, thus giving a more faithful representation of 3D scenes. However, it is indisputable that there is a need for light field displays on which the data can be natively visualised, fuelled by the recent innovations in the realm of acquisition and compression of light field contents. Assessing the visual quality of light field contents on native light field display is of extreme importance in future development of both new rendering methods, as well as new compression solutions. However, the limited availability of light field displays restrict the possibility of using them to carry out subjective tests. Moreover, hardware limitations in prototype models may lessen considerably the perceptual quality of experience in consuming light field contents. In this paper, we compare three different compression approaches for multi-layer displays, through both objective quality metrics and subjective quality assessment. Furthermore, we analyze the results obtained through subjective tests conducted using a prototype multi-layer display, and a recently-proposed framework to conduct quality assessment of light field contents rendered through a tensor display simulator in 2D screens. Using statistical tools, we assess the correlation among the two settings and we draw useful conclusions for future design of compression solutions and subjective tests for light field contents with muti-layer rendering.

关键词： Light field multilayer display tensor display subjective quality evaluation objective quality evaluation

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Real-time fusion of visible and thermal infrared images in surveillance applications on SoC hardware. 2

Real-time fusion of visible and thermal infrared images in s...

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conference on Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II

作者： Moren, Konrad Perschke, Thomas Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (数字)9781510621886

ISBN: (纸本)9781510621886

Fusion of thermal infrared and visual images is an important technique for real-time surveillance applications. Since image fusion is used in many real-time night vision applications such as target detection, recognition and tracking, it is important to understand the processing requirements and provide computationally efficient methods. In this paper, we present a real-time image fusion system designed for night vision supervisory and monitoring purposes. The system is equipped with two image sensors: TV and IR (thermal infrared). We implement a processing pipeline on the NVIDIA Tegra Tx2. The Tx2 platform is equipped with a many-core NVIDIA GPU and multi-core ARM CPU. Additionally, we present the system architecture as well as the design process of the efficient, real-time multi-spectral signal-processing algorithm. The algorithm is based on the second-generation wavelets also called lifting scheme. We show also a novel parallelization approach to perform the calculations in place, so no auxiliary memory is needed. This enables a fast parallel and pipelined processing flow. We achieve a considerable speedup compared to an optimized CPU implementation. The experimental results show that the fusion system can realize real-time image fusion processing for dual channels images at the rate of 30 frames per second for the Full-HD images.

关键词： image Fusion DWT GPGPU Multicore CPU SoC

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Analysis Of Brain Tumor Segmentation using Convolutional Neural Network with Magnetic Resonance Imaging

Analysis Of Brain Tumor Segmentation using Convolutional Neu...

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International conference on Communications and signal processing

作者： B.Keerthi Samhitha P. Yaswanth Yellappa Gari Srinivasulu Suja Cherukullapurath Mana Jithina Jose Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

Computer vision plays a vital role in human health care area. The various applications of these techniques have been playing a key role in reducing physicians judgment in diagnosis. Especially, in the case of brain related cases, it is suggested and required to take utmost care and a small error in diagnosis leads to disastrous. Although various methods like x ray, ct scan are available, mri is more reliable and danger free. High contrast images of brain tissues can be taken by using mri technique. Brain Tumor segmentation involves separation of tumors from normal brain tissues. The segmentation process helps in fetching required information for the purpose of diagnosis and what treatment has to be taken. It plays a vital role in medical image processing. The process involves risk because of abnormal form and irregularities at the boundaries. Determination of these tumors at the earlier stage helps to give better treatment to the patients. Usually, Physicians deal with the affected people by performing manual way of segmentation which results in the absence of time management. To overcome this manual segmentation problem, we developed CNN method of segmentation procedure which performs well by taking various factors like accuracy, recall and precision into consideration. MRI is one of the conservative techniques for prior diagnostic tool of brain tumor. The necessary measures for diagnosis and treatment planning have to be taken place quickly if the tumor is malignant in order to enhance successful treatment. By applying the following method, the doctors are capable of making accurate decision and required treatment can be done in an effective manner. The computer aided diagnostic system, MRI helps in reduction of workload by doctors.

关键词： Tumors image segmentation Magnetic resonance imaging Clustering algorithms Classification algorithms Feature extraction Convolutional neural networks

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Unsupervised Automatic Target Generation Process via Compressive Sensing

Unsupervised Automatic Target Generation Process via Compres...

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conference on Big Data - Learning, Analytics, and applications

作者： Bekit, Adam Della Porta, Charles Lampe, Bernard xue, Bai Chang, Chein-, I Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)9781510626447

Unsupervised target generation for hyperspectral imagery (HSI) have generated great interest in the hyperspectral community. However, most of the current unsupervised target generation algorithms have to process large HSI data, which is acquired using the traditional Nyquist-Shannon sampling theorem, resulting in data with high band-to-band correlation. As a consequence, these algorithms end up processing redundant information, raising the demand for large memory storage, processing time, and transmission bandwidth. In the past, some efforts have been dedicated to dealing with the redundant information via data reduction (DR) or data compression post-acquisition. However, to the best of our knowledge, this challenge has been addressed outside the context of Compressive Sensing (CS). This paper applies CS data acquisition process at the sensor level so that the redundant information is removed at the early stage of the data processing chain. The main advantage of our approach is that it employs a random sensing process, and the concept of universality, to randomly sense the HSI bands and produce data containing the bare minimum information. We take advantage of CS Restricted Isometric Properties (RIP), Restricted Conformal Properties (RCP), and newly derived orthogonal sub-space projection (OSP) properties to perform automatic target generation process (ATGP) in the compressively sensed band domain (CSBD), instead of in the original data space (ODS), where the HSI data contains full spectral bands. Our experimental results show that, by working in the CSBD, we avoid processing redundant data and still maintain performance results that are comparable with the performance results obtained in the ODS.

关键词： Compressive sensing (CS) automatic target generation process (ATGP) compressively sensed band domain (CSBD) original data space (ODS) orthogonal sub-space projection (OSP) Restricted Orthogonal Sub-space Projection Property (ROSPP)

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