A Low Complexity Switched Method for Enhancement of Spatial Resolution of Images

2012 ◽  
Vol 433-440 ◽  
pp. 6534-6539
Author(s):  
Hari Singh Choudhary ◽  
Vineet Khanna ◽  
Prakrati Trivedi
Keyword(s):  
Computational Complexity ◽  
Spatial Resolution ◽  
Visual Quality ◽  
Low Complexity ◽  
Interpolation Algorithm ◽  
Hybrid Scheme ◽  
Low Computational Complexity ◽  
Single Pass ◽  
Novel Approach ◽  
Block Based

This paper presents a novel approach of image interpolation based on the switching of new edge directed interpolation (NEDI) and single pass interpolation algorithm ( SPIA ) and switching is based upon the % of edges present in the blocks of the image. The switching of this interpolation algorithm is block based instead of image based or pixel based. Imperially we found that NEDI methods is better applicable for smoother images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the process interpolation. So, a hybrid scheme of combining NEDI method and SPIA method is used for better prediction of HR image. The proposed algorithm produces the better results for different varieties of images in terms of both PSNR measurement and subjective visual quality with low computational complexity as compare to recently developed interpolation algorithms.

A Low-complexity Minimum-variance Beamformer Based on Orthogonal Decomposition of the Compounded Subspace

2020 ◽  
Vol 43 (1) ◽  
pp. 3-18
Author(s):  
Yinmeng Wang ◽  
Yanxing Qi ◽  
Yuanyuan Wang
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Spatial Resolution ◽  
Low Complexity ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Orthogonal Decomposition ◽  
In Vivo Studies ◽  
Total Output ◽  
Accurate Estimation

Minimum-variance (MV) beamforming, as a typical adaptive beamforming method, has been widely studied in medical ultrasound imaging. This method achieves higher spatial resolution than traditional delay-and-sum (DAS) beamforming by minimizing the total output power while maintaining the desired signals. However, it suffers from high computational complexity due to the heavy calculation load when determining the inverse of the high-dimensional matrix. Low-complexity MV algorithms have been studied recently. In this study, we propose a novel MV beamformer based on orthogonal decomposition of the compounded subspace (CS) of the covariance matrix in synthetic aperture (SA) imaging, which aims to reduce the dimensions of the covariance matrix and therefore reduce the computational complexity. Multiwave spatial smoothing is applied to the echo signals for the accurate estimation of the covariance matrix, and adaptive weight vectors are calculated from the low-dimensional subspace of the original covariance matrix. We conducted simulation, experimental and in vivo studies to verify the performance of the proposed method. The results indicate that the proposed method performs well in maintaining the advantage of high spatial resolution and effectively reduces the computational complexity compared with the standard MV beamformer. In addition, the proposed method shows good robustness against sound velocity errors.

scholarly journals A Dynamic Predictive Search Algorithm for Fast Block-Based Motion Estimation

2021 ◽  
Author(s):  
Behnaz Abdoli
Keyword(s):  
Computational Complexity ◽  
Motion Estimation ◽  
Compression Ratio ◽  
Video Processing ◽  
Search Algorithm ◽  
Fast Motion Estimation ◽  
Low Computational Complexity ◽  
Video Codecs ◽  
Fast Motion ◽  
Block Based

Predictive fast Motion Estimation (ME) algorithms have been widely used in video CODECs due to their performance efficiency and low computational complexity. In this thesis, a new block-based fast motion estimation technique named Dynamic Predictive Search Algorithm (DPSA) is developed, which can be considered in predictive zonal search category. The proposed approach is based on the observation that temporally and spatially adjacent macro-blocks are not just statically correlated, but also dynamic alterations in their motion content are highly coherent. DPSA introduces a new set of six candidate predicted motion vectors. For early termination criteria, DPSA modifies termination procedure of already existing EPZS algorithm. Performance of this newly proposed algorithm has been compared to four other state-of-the-art algorithms implemented on JVT, H.264 standard software platform. Experimental results have proven that DPSA accomplishes up to 38% compression ratio enhancement achieved by a process with more 14.75% less computational complexity and up to0.47 dB higher PSNR values over the EPZS. It also manages to have up to 13% speed up over EPZS algorithm. Because of its simplicity and low computational complexity DPSA is energy efficient for portable video processing in computation- or power-constrained applications and easy to be implemented on both FPGA- and Microcontroller-based embedded systems. Also, higher compression ratio makes DPSA more compatible with limited capacity storage media, and limited band-width transmission networks.

scholarly journals A Dynamic Predictive Search Algorithm for Fast Block-Based Motion Estimation

2021 ◽  
Author(s):  
Behnaz Abdoli
Keyword(s):  
Computational Complexity ◽  
Motion Estimation ◽  
Compression Ratio ◽  
Video Processing ◽  
Search Algorithm ◽  
Fast Motion Estimation ◽  
Low Computational Complexity ◽  
Video Codecs ◽  
Fast Motion ◽  
Block Based

Predictive fast Motion Estimation (ME) algorithms have been widely used in video CODECs due to their performance efficiency and low computational complexity. In this thesis, a new block-based fast motion estimation technique named Dynamic Predictive Search Algorithm (DPSA) is developed, which can be considered in predictive zonal search category. The proposed approach is based on the observation that temporally and spatially adjacent macro-blocks are not just statically correlated, but also dynamic alterations in their motion content are highly coherent. DPSA introduces a new set of six candidate predicted motion vectors. For early termination criteria, DPSA modifies termination procedure of already existing EPZS algorithm. Performance of this newly proposed algorithm has been compared to four other state-of-the-art algorithms implemented on JVT, H.264 standard software platform. Experimental results have proven that DPSA accomplishes up to 38% compression ratio enhancement achieved by a process with more 14.75% less computational complexity and up to0.47 dB higher PSNR values over the EPZS. It also manages to have up to 13% speed up over EPZS algorithm. Because of its simplicity and low computational complexity DPSA is energy efficient for portable video processing in computation- or power-constrained applications and easy to be implemented on both FPGA- and Microcontroller-based embedded systems. Also, higher compression ratio makes DPSA more compatible with limited capacity storage media, and limited band-width transmission networks.

scholarly journals Block-based compressed sensing of MR images using multi-rate deep learning approach

2021 ◽  
Author(s):  
Ejaz Ul Haq ◽  
Huang Jianjun ◽  
Xu Huarong ◽  
Kang Li
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Compressive Sensing ◽  
Sampling Rate ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Blocking Artifacts ◽  
Image Block ◽  
Low Computational Complexity ◽  
Block Based

AbstractDeep learning (DL) models are highly research-oriented field in image compressive sensing in the recent studies. In compressive sensing theory, a signal is efficiently reconstructed from very small and limited number of measurements. Block-based compressive sensing is most promising and lenient compressive sensing (CS) approach mostly used to process large-sized videos and images: exploit low computational complexity and requires less memory. In block-based compressive sensing, a number of deep models are needed to train with each corresponding to different sampling rate. Compressive sensing performance is highly degraded through allocating low sampling rates to various blocks within same image or video frames. In this work, we proposed multi-rate method using deep neural networks for block-based compressive sensing of magnetic resonance images with performance that greatly outperforms existing state-of-the-art methods. The proposed approach is capable in smart allocation of exclusive sampling rate for each block within image, based on the image information and removing blocking artifacts in reconstructed MRI images. Each image block is separately sampled and reconstructed with different sampling rate and reassembled into a single image based on inter-correlation between blocks, to remove blocking artifacts. The proposed method surpasses the current state-of-the-arts in terms of reconstruction speed, reconstruction error, low computational complexity, and certain evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and relative l2-norm error (RLNE).

scholarly journals Low Complexity Rotation Algorithm for Reducing PAPR in Partial Transmits Sequence

2021 ◽  
Author(s):  
Thamer Alameri ◽  
Nabeel ali ◽  
Mothana Attiah ◽  
Mohammed Saad Talib ◽  
Jawad Mezaal
Keyword(s):  
Computational Complexity ◽  
Orthogonal Frequency Division Multiplexing ◽  
Average Power ◽  
Low Complexity ◽  
Papr Reduction ◽  
Frequency Division Multiplexing ◽  
Power Ratio ◽  
Frequency Division ◽  
Complexity Level ◽  
Novel Approach

Abstract High peak to average power ratio (PAPR) is considered as a prime challenge in orthogonal frequency division multiplexing. The partial transmits sequence (PTS) technique is one of the most effective methods for restraining the PAPR pattern. This study proposes a novel approach for enhancing PAPR reduction performance in a PTS by partitioning each subblock into two parts then exchanging the first sample with the last selection in each part of the subblock to generate a new partitioning scheme. The proposed algorithm is analysed and applied to typical traditional segmentation schemes, namely, the adjacent, interleaving and pseudo-random schemes. Moreover, simulation is conducted with two scenarios in which the number of subcarriers is set to 128 and 256. In both systems, the improved segmentation schemes demonstrate PAPR reduction performance that is superior to that of the traditional strategies. Furthermore, the computational complexity level of the enhanced adjusted PTS scheme is low compared with that of the conventional schemes.

scholarly journals Leveraging Graphical User Interface Automation for Generic Robot Programming

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Tudor B. Ionescu
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Programming Models ◽  
Shop Floor ◽  
Robot Programming ◽  
User Interactions ◽  
Programming Environments ◽  
Automated Generation ◽  
Novel Approach ◽  
Block Based

A novel approach to generic (or generalized) robot programming and a novel simplified, block-based programming environment, called “Assembly”, are introduced. The approach leverages the newest graphical user interface automation tools and techniques to generate programs in various proprietary robot programming environments by emulating user interactions in those environments. The “Assembly” tool is used to generate robot-independent intermediary program models, which are translated into robot-specific programs using a graphical user interface automation toolchain. The generalizability of the approach to list, tree, and block-based programming is assessed using three different robot programming environments, two of which are proprietary. The results of this evaluation suggest that the proposed approach is feasible for an entire range of programming models and thus enables the generation of programs in various proprietary robot programming environments. In educational settings, the automated generation of programs fosters learning different robot programming models by example. For experts, the proposed approach provides a means for generating program (or task) templates, which can be adjusted to the needs of the application at hand on the shop floor.

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