Global–local image enhancement with contrast improvement based on weighted least squares

Huang, Zhenghua; Zhu, Zifan; An, Qing; Wang, Zhicheng; Hao, Fang

doi:10.1016/j.ijleo.2021.167433

Cited by 11 publications

(3 citation statements)

References 38 publications

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“… The first column is the input images, the second to the fourth column2 are results of Ref. [ 45 – 47 ], respectively. The last column is the results of the proposed approach.…”

Section: Resultsmentioning

confidence: 99%

A color image contrast enhancement method based on improved PSO

et al. 2023

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Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle’s flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

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“… The first column is the input images, the second to the fourth column2 are results of Ref. [ 45 – 47 ], respectively. The last column is the results of the proposed approach.…”

Section: Resultsmentioning

confidence: 99%

A color image contrast enhancement method based on improved PSO

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Then, Wavelet Threshold and Histogram Equalization are performed on the original dataset to enhance the extraction of regional features. Finally, rotating, contrast adjustment and flipping of the defect image after denoising enhancement are carried out to increase the number of experimental samples, which prevents over-fitting phenomenon during model training and improves the generalization and robustness of the model [19]. Robustness refers to the ability of the model to maintain stable performance when the image data changes.…”

Section: Dataset Preprocessingmentioning

confidence: 99%

Surface Defect Image Classification of Lithium Battery Pole Piece Based on Deep Learning

MAO,

LI,

TAO

et al. 2023

IEICE Trans. Inf. & Syst.

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Aiming at the problem of low classification accuracy of surface defects of lithium battery pole pieces by traditional classification methods, an image classification algorithm for surface defects of lithium battery pole piece based on deep learning is proposed in this paper. Firstly, Wavelet Threshold and Histogram Equalization are used to preprocess the detect image to weaken influence of noise in non-defect regions and enhance defect features. Secondly, a VGG-InceptionV2 network with better performance is proposed by adding InceptionV2 structure to the improved VGG network structure. Then the original data set is expanded by rotating, flipping and contrast adjustment, and the optimal value of the model hyperparameters is determined by experiments. Finally, the model in this paper is compared with VGG16 and GoogLeNet to realize the recognition of defect types. The results show that the accuracy rate of the model in this paper for the surface pole piece defects of lithium batteries is 98.75%, and the model parameters is only 1.7M, which has certain significance for the classification of lithium battery surface pole piece defects in industry.

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“…In [3], the loss in localization accuracy induced by time di erence of arrival noises and velocity errors is reduced by the constrained total least squares method. In [4], to deal with the problem of overenhancement results, an image enhancement scheme based on weighted least squares are proposed. In [5], due to the complex biochemical characteristics of the wastewater treatment process, an adaptive dynamic nonlinear partial least squares model is proposed to improve the prediction performance and stability of e uent quality indexes.…”

Section: Introductionmentioning

confidence: 99%

Regularized Least Squares Recursive Algorithm with Forgetting Factor for Identifying Parameters in the Grinding Process

Deng

et al. 2022

Journal of Mathematics

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This paper investigates a parameter identification problem in the grinding process. Due to the data saturated phenomenon and the ill-posed of parameter identification inverse problem, this paper presents a regularized least squares recursive algorithm with a forgetting factor (RLSRAFF), the basic idea of which is to combine the forgetting factor with regularization parameters. Moreover, based on RLSRAFF, this paper verifies the recursive calculation of criterion function, analyzes the effect of calculation error from the gain matrix and proves the convergence of the proposed algorithm. Finally, effectiveness of RLSRAFF is verified by simulation experiments and grinding data. Compared with other algorithms, RLSRAFF can give a more convergence rate to the real data and reduce the error from the true value.

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Global–local image enhancement with contrast improvement based on weighted least squares

Cited by 11 publications

References 38 publications

A color image contrast enhancement method based on improved PSO

A color image contrast enhancement method based on improved PSO

Surface Defect Image Classification of Lithium Battery Pole Piece Based on Deep Learning

Regularized Least Squares Recursive Algorithm with Forgetting Factor for Identifying Parameters in the Grinding Process

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