Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement

Singh, Himanshu; Kumar, Anil; Balyan, L. K.; Singh, Girish Kumar

doi:10.1016/j.compeleceng.2017.06.029

Cited by 58 publications

(29 citation statements)

References 17 publications

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“…In the histograms of dark images with low light intensity and of bright images with high light intensity, the distribution of pixels is inclined to one side or concentrated at a specific position [35]. For an image with a concentrated pixel distribution, it is difficult to distinguish the structure from the background because the pixels have similar light intensities [36,37]. Therefore, in this study, the structure was distinguished from the background by performing histogram transformation to measure the response of the tubular level gauge.…”

Section: Image Enhancementmentioning

confidence: 99%

Non-Contact Water Level Response Measurement of a Tubular Level Gauge Using Image Signals

Kim

Park

Jeon

et al. 2020

Sensors

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The occurrence of excessive fluid sloshing during an earthquake can damage structures used to store fluids and can induce secondary disasters, such as environmental destruction and human casualties, due to discharge of the stored fluids. Thus, to prevent such disasters, it is important to accurately predict the sloshing behavior of liquid storage tanks. Tubular level gauges, which visually show the fluid level of a liquid storage tank, are easy to install and economical compared to other water level gauges. They directly show the fluid level and can be applied for various fluids because they can be constructed with various materials according to the fluid characteristics and the intended use. Therefore, in this study, the shaking table test was conducted to verify the validity of the method for measuring the water level response of the tubular level gauge installed on a liquid storage tank using image signals. In addition, image enhancement methods were applied to distinguish between the float installed in the tubular level gauge and the gray level of the background.

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Section: Image Enhancementmentioning

confidence: 99%

Non-Contact Water Level Response Measurement of a Tubular Level Gauge Using Image Signals

Kim

Park

Jeon

et al. 2020

Sensors

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show abstract

“…37 In this paper, partial involvement of the optimally weighted uniformly equalized interim intensity channel has been also proposed for preventing the intensities' probability distribution. 37 In this paper, partial involvement of the optimally weighted uniformly equalized interim intensity channel has been also proposed for preventing the intensities' probability distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…37 Considering γ = 1 implies to a one-to-one mapping that gives original input intensity channel as both compressed and expanded channel. 37 Considering γ = 1 implies to a one-to-one mapping that gives original input intensity channel as both compressed and expanded channel.…”

Section: The Conception Of the Proposed Histogram Equalization-based mentioning

confidence: 99%

“…Unlike employing single Gamma value in the traditional Gamma correction methods, an adaptive procedure is proposed for both gamma expanded and gamma compressed image based on weighted summation configuration. 37 In this paper, partial involvement of the optimally weighted uniformly equalized interim intensity channel has been also proposed for preventing the intensities' probability distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A good interval for the coefficient b is [0, 4). 37 Considering γ = 1 implies to a one-to-one mapping that gives original input intensity channel as both compressed and expanded channel.…”

Section: The Conception Of the Proposed Histogram Equalization-based mentioning

confidence: 99%

See 2 more Smart Citations

Using chaos world cup optimization algorithm for medical images contrast enhancement

Wang

Liu

Jimenez

2019

Concurrency and Computation

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In this paper, a chaos-based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state-of-the-art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.

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Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images

Singh

Kumar

Balyan

2020

Applications of Hybrid Metaheuristic Algorithms for Image Processing

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Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement

Cited by 58 publications

References 17 publications

Non-Contact Water Level Response Measurement of a Tubular Level Gauge Using Image Signals

Non-Contact Water Level Response Measurement of a Tubular Level Gauge Using Image Signals

Using chaos world cup optimization algorithm for medical images contrast enhancement

Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images

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