Adaptive Image Enhancement Algorithm CombiningKernel Regression and Local Homogeneity

Yang, Yu-Qian; Zhang, Jiangshe; Huang, Xing-Fang

doi:10.1155/2010/693532

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…Linear/nonlinear regression models are the typically parametric and the Artificial Neural Network (ANN) can be considered as an advanced form of nonlinear parametric model. In the latter class, there is no restriction on the functional form of the regression model; the model generally stores all the collected data instances in its memory and utilizes such memory to predict unknown input data [24,35]. Therefore, the approach of nonparametric learning is also called instance-based learning.…”

Section: Instance-based Regressionmentioning

confidence: 99%

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

Hoang

Tran

et al. 2017

Applied Computational Intelligence and Soft Computing

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This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.

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Section: Instance-based Regressionmentioning

confidence: 99%

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

Hoang

Tran

et al. 2017

Applied Computational Intelligence and Soft Computing

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“…It remaps the gray levels based on the probability distribution of the input gray levels. It flattens and stretches the dynamic range of the images histogram which results in overall contrast improvement [2]. HE methods can be categorized into two methods: improved global HE methods (GHE) and adaptive HE (AHE) methods.…”

Section: Introductionmentioning

confidence: 99%

“…Generate the input histograms and for lower and upper sub-images separately.Step 3. Find an uniform histogram for lower sub-image using (1),(2) and(3).Step 4. Obtain an optimal value of the contrast enhancement parameters 1 and 2 for lower and upper sub-images using optimization PSO procedure.…”

mentioning

confidence: 99%

Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement

Babu

Rajamani

Balasubramanian

2015

Mathematical Problems in Engineering

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A novel approach, Multipeak mean based optimized histogram modification framework (MMOHM) is introduced for the purpose of enhancing the contrast as well as preserving essential details for any given gray scale and colour images. The basic idea of this technique is the calculation of multiple peaks (local maxima) from the original histogram. The mean value of multiple peaks is computed and the input image’s histogram is segmented into two subhistograms based on this multipeak mean (mmean) value. Then, a bicriteria optimization problem is formulated and the subhistograms are modified by selecting optimal contrast enhancement parameters. While formulating the enhancement parameters, particle swarm optimization is employed to find optimal values of them. Finally, the union of the modified subhistograms produces a contrast enhanced and details preserved output image. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy, natural image quality evaluator, and absolute mean brightness error.

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“…The main objective of an image enhancement is to bring out the hidden image details or to increase the image contrast with a new dynamic range. Histogram equalization (HE) is one of the most popular techniques used for image contrast enhancement, since HE is computationally fast and simple to implement [1,2]. HE performs its operation by remapping the gray levels of the image based on the probability distribution of the input gray levels.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

Magudeeswaran

Ravichandran

2013

Mathematical Problems in Engineering

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Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC) and natural image quality evaluator (NIQE) index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.

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Adaptive Image Enhancement Algorithm CombiningKernel Regression and Local Homogeneity

Cited by 11 publications

References 19 publications

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning

Multipeak Mean Based Optimized Histogram Modification Framework Using Swarm Intelligence for Image Contrast Enhancement

Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

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