Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Chatterjee, Arpitam; Tudu, Bipan; Paul, Kanai Ch.

doi:10.1016/j.jvcir.2012.09.001

Cited by 9 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In traditional BPSO [17,18], the next position of each particle is calculated according to the following formula:…”

Section: Parameter Optimization Of Knn Based On Ibpsomentioning

confidence: 99%

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Fei

2019

Shock and Vibration

View full text Add to dashboard Cite

Fault diagnosis of bearing based on variational mode decomposition (VMD)-phase space reconstruction (PSR)-singular value decomposition (SVD) and improved binary particle swarm optimization (IBPSO)-K-nearest neighbor (KNN) which is abbreviated as VPS-IBPSOKNN is presented in this study, among which VMD-PSR-SVD (VPS) is presented to obtain the features of the bearing vibration signal (BVS), and IBPSO is presented to select the parameter K of KNN. In IBPSO, the calculation of the next position of each particle is improved to fit the evolution of the particles. The traditional KNN with different parameter K and trained by the training samples with the features based on VMD-SVD (VS-KNN) can be used to compare with the proposed VPS-IBPSOKNN method. The experimental result demonstrates that fault diagnosis ability of bearing of VPS-IBPSOKNN is better than that of VS-KNN, and it can be concluded that fault diagnosis of bearing based on VPS-IBPSOKNN is effective.

show abstract

“…In traditional BPSO [17,18], the next position of each particle is calculated according to the following formula:…”

Section: Parameter Optimization Of Knn Based On Ibpsomentioning

confidence: 99%

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Fei

2019

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Halftoning techniques aim for reproducing continuous-tone images c∈[0,1]N with binary pixels h∈{0,1}N, where N denotes the number of pixels. In addition to classic approaches like ordered dithering, 1 – 4 error diffusion, 5 – 12 and search-based methods, 13 – 18 recently, deep learning-based solutions 19 – 25 are showing their abilities in rendering decent halftones with reversibility 21 or less computational complexity 23 . Specifically, convolutional neural networks (CNNs) are trained to project white Gaussian noise maps into halftone pixels conditioning on the continuous-tone image [illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast halftoning policy network with error feedback

Jiang

Xiong

Yin³

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

Deep learning-based halftoning methods have demonstrated their abilities in dithering images with desired blue-noise properties. However, tons of parameters and computations come along with the 2D convolutional neural networks (CNNs), which hinder their practical usage, particularly in the cost-sensitive printing imaging system equipped with low-end computing devices. We devise a fast halftoning policy network to alleviate the problem. Motivated by classic error diffusion algorithms, we introduce an error feedback mechanism to model the dependencies between halftone lines. Consequently, the problem collapses to 1D deep halftoning within each line, which simplifies the learning task. Furthermore, a very simple convolutional layer is applied to capture the texture details from the continuous-tone image. We find that the proposed halftoning model can stably converge under the reinforcement learning-based framework. Experiments show that our model achieves a 10.6× speedup with only 1.7% parameters compared to a previous 2D CNN-based work while maintaining similar halftone quality. We have also conducted experiments on a digital signal processor to demonstrate how the proposed model saves expensive off-chip memory accesses and improves processing throughput in real scenarios.

show abstract

“…The BinPSO algorithm retains the same advantages of the original PSO algorithm proposed in [8] and it allows for solving optimization problems of discrete binary nature. The BinPSO algorithm has been modified and implemented to solve several real-world problems [1,14,15,17,20].…”

Section: Introductionmentioning

confidence: 99%

Performance Investigation on Binary Particle Swarm Optimization for Global Optimization

Lee

El‐Saleh

Loo³

et al. 2015

Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection

View full text Add to dashboard Cite

Abstract. Binary particle swarm optimization (BinPSO) is introduced as a population-based random search algorithm for discrete binary optimization problems. A number of BinPSO variants have been introduced in the literature and showed performance improvements over the original BinPSO algorithm. However, no detailed performance comparison between these BinPSO variants has been found in the current literature. In this paper, a more thorough performance comparison study on the BinPSO variants in terms of convergence speed, solution quality and performance stability is presented. The BinPSO variants are further compared with a newly adopted cooperative BinPSO variant. The performance evaluation is conducted using De Jong's test functions, several complex multimodal functions, and a real-world engineering problem, namely optimization of the detection performance of cooperative spectrum sensing in cognitive radio networks. Results show that most of the BinPSO variants exhibit excellent performance on solving De Jong's test functions while the cooperative BinPSO variant performs better on the complex multimodal problems and the real-world engineering problem. Overall, the cooperative BinPSO variant shows the most promising performance, especially in terms of solution quality and performance stability.

show abstract

Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Cited by 9 publications

References 33 publications

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

The Hybrid Method of VMD‐PSR‐SVD and Improved Binary PSO‐KNN for Fault Diagnosis of Bearing

Fast halftoning policy network with error feedback

Performance Investigation on Binary Particle Swarm Optimization for Global Optimization

Contact Info

Product

Resources

About