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image denoising based on Swin Transformer Residual Conv U-Net 27

Image denoising based on Swin Transformer Residual Conv U-Ne...

引用

27th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Gan, Yong Zhou, Shaohui Chen, Haonan Wang, Yuefeng Zhengzhou Univ Technol Zhengzhou Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at sigma = 15, 33.87 and 0.915 at sigma = 25, and 28.96 and 0.810 at sigma = 50.

关键词： image Denoising Deep Learning Transformer Convolutional Neural Networks U-Net

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parallel algebraic reconstruction technique for computed tomography

Parallel algebraic reconstruction technique for computed tom...

引用

International conference on parallel and distributed processing Techniques and Applications

作者： Melvin, C Thulasiraman, P Gordon, R Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 2N2 Canada

ISBN: (纸本)1892512459

Algebraic Reconstruction Technique (ART) is a method for reconstructing images from projections. It is widely used in applications such as Computed Tomography (CT). The algorithm requires fewer views, and hence less radiation, to produce a satisfactory image. However,, the approach is not widely used due to the computational intensive nature of the problem. In this paper we design and develop a parallel ART (PART) algorithm and study its performance on a network of workstations using the Message Passing Interface(MPI).

关键词： image processing computed tomography algebraic reconstruction technique MPI parallel algorithm

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Spiral counting on a rectangular lattice

Spiral counting on a rectangular lattice

引用

International conference on parallel and distributed processing Techniques and Applications

作者： Sheridan, P Griffith Univ Sch Comp & Informat Technol Nathan Qld 4111 Australia

ISBN: (纸本)1932415262

This paper investigates a class of image algebras that result from a pixel-addressing scheme known as Spiral Counting. This class of algebras is important to the field of image processing and computer vision for two reasons: Firstly, they facilitate fundamental image transformations, rotation, scaling and translation. Secondly, they possess computational properties pertinent to discrete representations of images, digital image technology and biological vision. Two new forms of Spiral Counting named Pseudo Spiral Counting and Rectangular Spiral Counting is described. A comparison of the four forms of Spiral Counting is provided It is the conclusion of this paper that Rectangular Spiral Counting provides the greatest potential for general use as it combines the power of Spiral Architecture transformations with the existing weal,h of image processing algorithms.

关键词： spiral architecture image transformations pseudo-invariance

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MultiPADDI-2 board for image processing

MultiPADDI-2 board for image processing

引用

conference on parallel and distributed methods for image processing

作者： Srini, VP Chow, N Sutton, RA Rabaey, JM Data Flux Systems Inc. MS 292 Berkeley CA 94709 1678 Shattuck Ave. United States University of California EECS Dept. Berkeley CA 94720 United States

ISBN: (纸本)0819425885

We have constructed a prototype image processing board containing 384 processors In 8 VLSI chips. The goal of the prototype is to show how fine grain parallelism present in image processing applications can be exploited by using lots of simple processors inter-connected in clever ways. Each processor has a 16-bit data path, a simple instruction set containing 12 instructions, a simple control unit, and a scan chain for loading data and program, Each VLSI chip, called PADDI-2, contains 48 processors, The programming model used for the processors is MIMD. Each processor has 8 words in the instruction memory. There are internal registers and queues in a processor for storing data and partial results. Data is assumed to be entering the system as a stream and processed by the processors. Each VLSI chip is connected to an external memory (64K x 16). a hardware synchronization mechanism is used for communication between processors;memory, and the external environment. If a sender and receiver is within the same chip, communication can be done in one cycle by the hierarchical interconnect bus structure, Programming the processors and the interconnections are done at compile time. The board is interfaced to a Sun SPARCstation using the SBus. Video input and output is supported by the board and field buffers are used for buffering. Software tools for checking the board, running test programs at the assembly language level, and Libraries for application development have been produced. image processing applications are currently under development. The board is available for experimentation over the Internet. Further details are available from the project web page (http://***/spartan).

关键词： parallel DSP image processing data driven processor data streaming

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Radar image processing with clusters of computers

Radar image processing with clusters of computers

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2000 IEEE Aerospace conference

作者： Goller, A Leberl, F Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria

ISBN: (纸本)0780358465

Some radar image processing algorithms such as shape-from-shading are particularly compute-intensive and time consuming. If, in addition, a data set to be processed is large, then it may make sense to perform the processing of images on multiple workstations or parallel processing systems. We have implemented shape-from-shading, stereo matching, resampling, gridding and visualization of terrain models in such a manner that they execute either on parallel machines or on clusters of workstations. We were motivated by the large image data set from NASA's Magellan mission to planet Venus, but received additional inspiration from the European Union's Center for Earth Observation program (CEO) and Austria's MISSION initiative for distributed processing of remote sensing images on remote workstations, using publicly accessible algorithms. We have developed a multi-processor approach that we denote as CDIP for Concurrent and distributed image processing. The speedup for image processing tasks increases nearly linearly with the number of processors, be they on a parallel machine or arranged in a cluster of distributed workstations. Our approach adds benefits for users of complex image processing algorithms: the efforts for code porting and code maintenance are reduced and the necessity for specialized parallel processing hardware is eliminated.(1)

关键词： Aerospace engineering

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Convex set theoretic image recovery with inexact projection algorithms

Convex set theoretic image recovery with inexact projection ...

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International conference on image processing (ICIP 2001)

作者： Combettes, PL Univ Paris 06 Anal Numer Lab F-75005 Paris France

ISBN: (纸本)0780367251

In image recovery, convex projection methods have been in use for almost two decades. However, while it is well known that projections can seldom be computed exactly, the effect of inexact projections on the behavior of such methods has not yet been investigated. In this paper, we propose such an analysis and establish conditions on the projection errors under which the theoretical convergence properties of various algorithms remain valid. Our analysis covers sequential, parallel, and block-iterative (subgradient) projection methods for consistent and inconsistent set theoretic image recovery problems. It is shown in particular that parallel projection methods are more robust to errors than sequential methods such as the popular POCS algorithm.

关键词： image reconstruction

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Faster and more accurate edge detection on spiral architecture

Faster and more accurate edge detection on spiral architectu...

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International conference on Imaging Science, Systems and Technology (CISST 03)

作者： He, XJ Wu, Q Hintz, T Wang, HQ Univ Technol Sydney Dept Comp Syst Broadway NSW 2007 Australia

ISBN: (纸本)1892512483

In this paper, a parallel and distributed algorithm oil Spiral Architecture for edge detection is proposed The proposed algorithm is based on Master-Slave model. The master node uniformly separates an image using a Spiral Multiplication and sends each slave node a sub-image. Each slave node performs,e detection on the sub-image based on the edge gradient of image brightness function. image noise is suppressed by a convolution with Gaussian kernel before edge points are detected The gradient consists of three components in three diagonal directions. This detection scheme guarantees a well-balancing load among the slave nodes. Its processing speed is greatly reduced through simultaneous and parallel processes on sub-images. Its accuracy is enhanced by a better approximation of the gradient components.

关键词： spiral architecture distributed processing edge detection parallel processing distributed computing

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Performance of Krylov subspace methods for symmetric matrices in hybrid parallelization 25

Performance of Krylov subspace methods for symmetric matrice...

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25th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Abe, Kuniyoshi Fujino, Seiji Gifu Shotoku Univ Fac Econ & Informat Gifu 5008288 Japan Kyushu Univ Fukuoka 8128581 Japan

ISBN: (纸本)9781509060580

We deal with Krylov subspace methods such as the Conjugate Gradient (CG) method for solving linear equations with symmetric matrices on a parallel computer. The algorithm which has the less number of synchronization (abbreviated as synchro) points is crucial for reducing the communication time on the parallel computer. CG has two synchro points per iteration, and the AZMJ variant of Orthomin(2) (abbreviated as AZMJ) which has just one synchro point has been proposed. A number of strategies to generate preconditioners have been known for obtaining successful and rapid convergence. We apply the Symmetric Successive Over Relaxation (SSOR) preconditioner to their methods. Then extra computational costs are required and we need to compute the forward and back substitution in the preconditioned algorithm. We therefore propose an alternative SSOR splitting for the parallel computing, and a computation procedure to parallelize the forward and back substitution and to reduce the computational costs. The numerical results show that the convergence behavior of AZMJ is superior to that of CG, and the parallel performance of AZMJ, which has the less number of synchro points than CG, is higher using the hybrid parallelization on the parallel computer. AZMJ and CG with the preconditioner using our proposed procedure are efficient on the parallel computer, and are useful for obtaining rapid convergence.

关键词： Krylov subspace methods linear equations symmetric matrices hybrid parallelization parallel computer

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data distributed System Emotion analysis image classification parallel Programing Face expression recognition

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Proceedings of the International conference on parallel and distributed processing Techniques and Applications

Proceedings of the International Conference on Parallel and ...

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Proceedings of the International conference on parallel and distributed processing Techniques and Applications

ISBN: (纸本)1892512416

This Volume 1 of the conference proceedings contains 74 papers. Topics discussed include grid computing, coordination and component-oriented computing, infrastructures and applications for cluster and grid computing environments, new trends in distributed computing, algorithms for parallel computing models, massively parallel and distributed computing, scheduling, client-server computing, parallel/distributed computing strategies, models and alogrithms, parallel and distributed image processing, image retrieval, video and multimedia and load balancing/sharing.

关键词： parallel processing systems

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