The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
P.v. Vara Prasad Rao - One of the best experts on this subject based on the ideXlab platform.
-
Weighted Guided Image Filtering
International Journal of Research, 2016Co-Authors: Nagara Kavitha, P.v. Vara Prasad RaoAbstract:It is known that local Filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided Image Filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided Image Filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing Filters in the sense that: 1) the complexity of the WGIF is O(N) for an Image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing Filters. The WGIF is applied for single Image detail enhancement, single Image haze removal, and fusion of differently exposed Images. Experimental results show that the resultant algorithms produce Images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final Images with negligible increment on running times.
Devrim Akgün - One of the best experts on this subject based on the ideXlab platform.
-
CUDA Based Computation of Quadratic Image Filters
International Journal of Applied Mathematics Electronics and Computers, 2020Co-Authors: Devrim Akgün, Süleyman UzunAbstract:Image processing applications usually requires nonlinear methods due to the nonlinear characteristics of Images. Quadratic Image Filter which is a class of nonlinear Image Filters are widely used in practice such as noise elimination edge detection and Image enhancement. On the other hand, second order products of the pixels make quadratic Image Filters computationally expensive to implement when compared to linear convolution. In the last decade, CUDA accelerated computing has been widely used in Image processing applications to reduce computation times. In this study, an efficient method for the CUDA acceleration of the quadratic Image Filter has been implemented. For this purpose, alternative algorithms were examined comparatively since the performance of the GPU is sensitive to memory utilization. Because quadratic Filter has a large number of coefficients and quadratic terms, the algorithm which utilizes the shared memory for storing Image blocks provided the best throughput among the examined methods. Comparative results that were obtained using various Images in different sizes show significant accelerations over sequential implementation.
-
Comparative Analysis of Noise Filtering Performance of Quadratic Image Filters
2019Co-Authors: Süleyman Uzun, Devrim AkgünAbstract:Quadratic Image Filters are Filters belonging to a subclass of nonlinear model known as Volterra Filters. Because of the nonlinear characteristics of Images, nonlinear Image Filters generally produce better results than linear Filters. In the present study, performance of the Quadratic Image Filters for Gaussian noise is examined by comparing with Gaussian Filter and Median Filter. For this purpose, the mask weights used were determined by using Differential Evolution algorithm on synthetic training Images. Noise added colour test Images were Filtered using Quadratic Image Filter using the calculated weights and the results were compared with Gaussian Filter and Median Image Filter.
-
A practical parallel implementation for TDLMS Image Filter on multi-core processor
Journal of Real-Time Image Processing, 2017Co-Authors: Devrim AkgünAbstract:In this study, parallel implementation of adaptive Image Filtering algorithm based on two-dimensional least mean square method (TDLMS) where the weights are continuously adjusted during Filtering was realized by proposed design considerations. Despite its strictly sequential structure, the effect of a pixel on weights vanishes as the Filter mask progresses. Based on this property, the load of Filtering algorithm is allocated to threads by splitting the input Image into sub-blocks. Due to the discontinuities, the crossing distortions between sub-blocks were eliminated using weight synchronization with the neighbor sub-block. Performance evaluations for various sizes of Images were realized on a computer with multi-core processor using open multiprocessing library. In spite of the sequential nature of the algorithm, results show that the parallel implementation provides significant improvements in terms of both speedup and parallel efficiency.
-
Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter
International Journal of Applied Mathematics Electronics and Computers, 2016Co-Authors: Süleyman Uzun, Devrim AkgünAbstract:One of the widespread Image processing applications is Image Filtering with two dimensional convolution. Determining the weights of Image Filters are of importance for the success of Filtering operation. Heuristic algorithms such as genetic algorithms provide an efficient way of training these types of Filters. Due to the high computational cost of repetitive Image Filtering operations, this process may take hours to implement using single core computing. OpenMP (Open Multi Processing) provides an efficient library for utilizing the computing power of multicore processors. In this study, OpenMP accelerated training of separable Filters that are a subclass of convolution Filters has been implemented based on genetic algorithms. Comparative speed-up results for various sizes of Images using various sizes of Filtering kernels were presented. Also the effect of population size of genetic algorithm and the number of working cores have been investigated.
Liu Qi-zhong - One of the best experts on this subject based on the ideXlab platform.
-
Modeling and Simulation of Underwater Detection System Based on Imaging Sonar
Computer Simulation, 2006Co-Authors: Liu Qi-zhongAbstract:Underwater detection technology has been paid more and more attention in recent years,Aiming at the problems of high cost and uncertainties of the method existing in the study of underwater detection by experiments,this paper introduces the methods of modeling and simulation to design the simulation software system.Based on the software design of simulation system for underwater imaging sonar detection,algorithms of sub-modules are analyzed,several units such as Image creation,Image Filter and control system are paid more attention,several mathematic models and simulation results are presented.Then the simulation of underwater sonar imaging detection is realized.This study provides an economic,practical and precise research platform for underwater sonar imaging detection.
Nagara Kavitha - One of the best experts on this subject based on the ideXlab platform.
-
Weighted Guided Image Filtering
International Journal of Research, 2016Co-Authors: Nagara Kavitha, P.v. Vara Prasad RaoAbstract:It is known that local Filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided Image Filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided Image Filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing Filters in the sense that: 1) the complexity of the WGIF is O(N) for an Image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing Filters. The WGIF is applied for single Image detail enhancement, single Image haze removal, and fusion of differently exposed Images. Experimental results show that the resultant algorithms produce Images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final Images with negligible increment on running times.
Tamio Arai - One of the best experts on this subject based on the ideXlab platform.
-
an integrated memory array processor architecture for embedded Image recognition systems
International Symposium on Computer Architecture, 2005Co-Authors: Shorin Kyo, Shinichiro Okazaki, Tamio AraiAbstract:Embedded processors for video Image recognition require to address both the cost (die size and power) versus real-time performance issue, and also to achieve high flexibility due to the immense diversity of recognition targets, situations, and applications. This paper describes IMAP, a highly parallel SIMD linear processor and memory array architecture that addresses these trading-off requirements. By using parallel and systolic algorithmic techniques, despite of its simple architecture IMAP achieves to exploit not only the straightforward per Image row data level parallelism (DLP), but also the inherent DLP of other memory access patterns frequently found in various Image recognition tasks, under the use of an explicit parallel C language (1DC). We describe and evaluate IMAP-CE, a latest IMAP processor, which integrates 128 of 100MHz 8 bit4-way VLIW PEs, 128 of 2KByte RAMs, and one 16 bit RISC control processor, into a single chip. The PE instruction set is enhanced for supporting 1DC codes. IMAP-CE is evaluated mainly by comparing its performance running 1DC codes with that of a 2.4GHz Intel P4 running optimized C codes. Based on the use of parallelizing techniques, benchmark results show a speedup of up to 20 for Image Filter kernels, and of 4 for a full Image recognition application.
