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A Double-stream Exchange Transformer Network for Intrinsic image Decomposition

A Double-stream Exchange Transformer Network for Intrinsic I...

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2022 international conference on image processing and Media Computing, ICIPMC 2022

作者： Zhang, Feng Jiang, Xiaoyue Xia, Zhaoqiang Gabbouj, Moncef Peng, Jinye Feng, Xiaoyi Northwestern Polytechnical University School of Electronics and Information Xi'an China Tampere University Department of Computing Sciences Tampere Finland Northwestern University School of Information Science and Technology Xi'an China

ISBN: (数字)9781665468725

ISBN: (纸本)9781665468725

Intrinsic image decomposition separates the input image into several layers which reflect the attributes of the scene. In this paper, we present a double-stream exchange transformer network for intrinsic image decomposition, in which an independent and interrelated relationship is built between reflectance and shading. There are two core designs in the double-stream exchange transformer network (DSETNet). First, we propose a novel exchange transformer block, which performs the information exchange and reconstruction between reflectance and shading components in a window-based self-attention. Second, a residual structure is added into the exchange transformer block to form a residual exchange transformer block to eliminate artifacts caused by local window areas. We predict reflectance and shading constraint relationship between reflectance and shading is established through residual exchange transformer block. The evaluation results on two real and synthetic public datasets BOLD and ShapeNet show that the DSETNet achieves competitive results with other advanced algorithms. © 2022 IEEE.

关键词： Reflection

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Towards image denoising in the latent space of learning-based compression 44

Towards image denoising in the latent space of learning-base...

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conference Applications of Digital image processing XLIV

作者： Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

In recent years, learning-based image compression has demonstrated similar or superior performance when compared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage of autoencoders, which are architectures consisting of two main parts: a multi-layer neural network encoder and its dual decoder. The encoder maps the input image represented in the pixel domain to a compact representation, also known as latent space. Consequently, the decoder reconstructs the original image in the pixel domain from its latent representation, as accurately as possible. Traditionally, image processing algorithms, and in particular image denoising, are applied to images in the pixel domain before compression, and eventually in some cases as a post-processing stage after decompression. In this context, the combination of denoising operations with the autoencoder might reduce the computational cost while achieving similar performance in accuracy. In this paper, the idea of combining the image denoising task with compression is examined. In particular, the integration of denoising convolutional layers in the decoder of a learning-based compression network is investigated. Results show that, while the rate-distortion performance of the method is slightly reduced, a gain in the computational complexity can be achieved.

关键词： image denoising learning-based compression compressed domain denoising latent space image processing

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image defogging system and algorithm research based on MATLAB platform

Image defogging system and algorithm research based on MATLA...

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2021 international conference on advanced Manufacturing Technology and Electronic Information, AMTEI 2021

作者： Chen, Yongchang Sun, Xiangrui Qiu, Xiaofeng Wang, Xinyan Department of Telecommunications Tibet University Tibet Lhasa850000 China Department of Civil Engineering and Water Conservancy Tibet University Tibet Lhasa850000 China

Since the Reform and Opening up, the characteristics of China's high-quality economic development have become more distinctive, the adjustment of economic and social structure has been continuously optimized, the kinetic energy has been continuously enhanced, and the degree of industrialization has been significantly improved. However, it has also caused some environmental problems, and haze pollution is one of them. The haze weather with low visibility has a certain impact on people's daily travel, transportation and navigation. Because the images taken under the haze condition will be degraded to varying degrees and the image details will be lost, the effect of identifying and processing the degraded images in the later stage will be reduced. Therefore, in order to clear and restore the degraded image, we developed a defogging system based on MATLAB software. The system mainly uses histogram equalization, MSR and dark channel a priori defogging algorithms to restore fog degraded images. The results indicate that various defogging algorithms are able to enhance the clarity of the picture to varying degrees. The successful implementation of the defogging system lays a theoretical foundation for the in-depth research and application of fog image defogging processing technology, and has good technical operability. © Published under licence by IOP Publishing Ltd.

关键词： Restoration

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A real-time image-based rendering framework

A real-time image-based rendering framework

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1st international conference on 3DTV

作者： Weigel, Christian Werner, Stefan Schuebel, Peter Tech Univ Ilmenau Inst Media Technol D-98684 Ilmenau Germany

ISBN: (纸本)9781424407217

We present a software framework designed for real-time image-based rendering applications. Due to its modular structure the framework suits for a number of tasks mainly in the computer vision area. First, we introduce the state of the art software design using parallel processing paradigms. Then, we show examples of 3DTV applications based on the system namely the preprocessing and synthesis of so called 3D video objects based on the theory of the trifocal transfer. We point out that due to the possibility of freely configuring the system the calculation of pre-processing and synthesis algorithms are accelerated significantly. Finally, we present possible future applications.

关键词： IBR 3D video object trifocal transfer real-time processing multithreading

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A No-reference image Quality Assessment Algorithm Based on Human Visual Perception Process Reconstruction 4

A No-reference Image Quality Assessment Algorithm Based on H...

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4th international conference on Video, signal and image processing, VSIP 2022

作者： Zhang, Shuo Su, Ping Ma, Jianshe Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781450397810

Traditional no-reference image quality assessment algorithms mostly focus on the objective indicators of the image, such as the IE (information entropy) and clarity of the image. These indicators can reflect the quality of the image at the objective level to a certain extent, but it cannot well reflect the human visual evaluation of the image. In fact, it's still a relatively difficult problem to obtain the human visual evaluation of the image through a no-reference image quality assessment algorithm, considering the complexity of the imaging mechanism of the HVS (Human Visual System), for example, the connection mode of photoreceptor cells and ganglion cells which still cannot be well expressed by any specific model, along with the individual differences of human visual perception. This paper starts with the perception process reconstruction of the HVS, uses the similarity between the reconstructed image and the original image to reflect whether the image conforms to the request of human visual perception, carries out parameter optimization and correlation verification. Finally, it is found that the proposed algorithm has better relevance, lightness and universality than the existing algorithms, and can more effectively restore the scoring result of human visual perception of the test image. © 2022 ACM.

关键词： image reconstruction

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Drone-vs-Bird Detection Challenge at ICIAP 2021 21st

Drone-vs-Bird Detection Challenge at ICIAP 2021

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21st international conference on image Analysis and processing (ICIAP)

作者： Coluccia, Angelo Fascista, Alessio Schumann, Arne Sommer, Lars Dimou, Anastasios Zarpalas, Dimitrios Sharma, Nabin Nalamati, Mrunalini Eryuksel, Ogulcan Ozfuttu, Kamil Anil Akyon, Fatih Cagatay Sahin, Kadir Buyukborekci, Efe Cavusoglu, Devrim Altinuc, Sinan Xing, Daitao Unlu, Halil Utku Evangeliou, Nikolaos Tzes, Anthony Nayak, Abhijeet Bouazizi, Mondher Ahmad, Tasweer Goncalves, Artur Rigault, Bastien Jain, Raghvendra Matsuo, Yutaka Prendinger, Helmut Jajaga, Edmond Rushiti, Veton Ramadani, Blerant Pavleski, Daniel Univ Salento Lecce Italy Fraunhofer IOSB Karlsruhe Germany Ctr Res & Technol Hellas CERTH Informat Technol Inst Thessaloniki Greece Sidney Univ Technol Sch Comp Sci Sydney Australia OBSS Technol Ankara Turkey NYU New York NY USA New York Univ Abu Dhabi U Arab Emirates Natl Inst Informat Tokyo Japan Univ Freiburg Freiburg Germany COMSATS Univ Islamabad Pakistan Keio Univ Yokohama Kanagawa Japan Optimays Inc Tokyo Japan Univ Tokyo Tokyo Japan Mother Teresa Univ Skopje North Macedonia

ISBN: (纸本)9783031133244;9783031133237

This paper reports the results of the 5th edition of the "Drone-vs-Bird" detection challenge, organized within the 21st international conference on image Analysis and processing (ICIAP). By taking as input video samples recorded by common cameras, the aim of the challenge is to devise advanced approaches aimed at spotlighting the presence of drones flying in the monitored area, while limiting the number of wrong alarms raised when similar flying entities such as birds suddenly appear in the scene. To this end, a number of important issues such as the dynamic variations in the scene and the background/foreground motion effects should be carefully considered, so as to allow the proposed solutions to correctly identify drones only when they are actually present. The paper summarizes the novel algorithms proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2022 challenge dataset.

关键词： Drone detection Deep learning image and video signal processing

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Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology 1st

Wirelessly Controlled Plant Health Monitoring and Medicate S...

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1st international conference on advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Ayalew, Lijaddis Getnet Mattihalli, Channamallikarjuna Asmare, Fanuel Melak Bahir Dar Univ Fac Elect & Comp Engn Bahir Dar Inst Technol Bahir Dar Ethiopia

ISBN: (纸本)9783030960407;9783030960391

The availability of good quality fruits and vegetables is paramount in preventing starvation and minimizing outbreaks of diseases which leads to improving quality of life. One of the major obstacles of the mentioned availability is plant leaf disease. Although manpower plays a vital role in detecting such problems it is time-intensive, expensive, and very inefficient. Thus, developing a mechanism to vigorously monitor leaf's health and detect diseases of plant leaves at early stages is mandatory so that one can produce plenty. In this contribution, a system that detects leaf disease is developed using image processing algorithms, the k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perception (MLP) machine learning algorithms are compared based on plant disease detection and classification systems performances. We also developed a prototype of simple-to-install technology that can recognize leaf diseases and allow medicine flow based on the results. This paper presents a smart plant health monitoring system that takes into account humidity, temperature, and soil contents.

关键词： image processing IoT KNN MLP SVM

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image denoising algorithms using norm minimization techniques 2

Image denoising algorithms using norm minimization technique...

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2nd international conference on Computer Science's Complex Systems and their Applications, ICCSA 2021

作者： Diffellah, Nacira Bekkouche, Tewfik Hamdini, Rabah Dept. of Electronics Faculty of Technology University of Bordj Bou Arreridj Bordj Bou Arreridj34000 Algeria Dept. of Electromechanics Faculty of Technology University of Bordj Bou Arreridj Bordj Bou Arreridj34000 Algeria Dept. of Electronics Faculty of Technology University of Saad Dahlab Blida 01 Blida09000 Algeria

image denoising is one of the fundamental image processing problems. Noise removal is an important step in the image restoration process. In this paper, firstly we develop and implement two different image denoising algorithms based on norm minimization, namely 1 and 2-regularization applied to images contaminated by Gaussian noise. Then, after their discretization and implementation, we perform a comparison between the two methods using several test images. Through this study, the algorithm which minimizes 2-norm of gradient of image has a unique solution and it's easy to implement, but it doesn't accept contour discontinuities, causing the obtained solution to be smooth. The 2-norm will blur the edges of the image. In order to preserve sharp edges, 1-norm is introduced. There are different methods to solve the problem of energy minimization. In this work, we have chosen the discretization finite difference method before applying the gradient descent algorithm to optimize the signal (2D grayscale images) denoising functionality. Experiments results, show that 1 regularization encourages image smoothness while allowing for presence of jumps and discontinuities, a key feature for image processing because of the importance of edges in human vision. © 2021.

关键词： image denoising

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An Optimizing Multilevel Thresholding for image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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16th international conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image segmentation

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Multi-scale pedestrian detection based on receptive field matching 21

Multi-scale pedestrian detection based on receptive field ma...

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5th international conference on Graphics and signal processing, ICGSP 2021

作者： Yan, Chaoqi Zhang, Hong Li, Xuliang Chen, Hao Yuan, Ding Image Processing Center Beihang University Beijing102206 China

ISBN: (纸本)9781450389419

Pedestrian detection is a hot topic in both academia and industry. However, the pedestrian in the image presents different scales due to the different distance from the fixed cameras. Thus, how to detect pedestrians of different scales has become an attractive problem in pedestrian detection field. In this paper, we propose a simple and compact multi-scale pedestrian detection architecture based on receptive field matching (denoted as RFMNet), which can cover continuous multi-scale pedestrians with 100% in theory. In this model, the receptive field is regarded as an invisible "anchor", so that the feature points with different scale receptive field can detect different scale pedestrian targets without any bells and whistles. Based on the above analysis, the proposed approach has an anchor-free setting. The extensive experiments on Caltech-USA benchmark demonstrate that our method outperforms the state-of-the-art pedestrian detection algorithms. © 2021 ACM.

关键词： Receptive field Pedestrian detection Caltech Anchor-free

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