2014 9th International Conference on Industrial and Information Systems (ICIIS) 2014

DOI: 10.1109/iciinfs.2014.7036526

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Entropy based optimized weighted histogram equalization for Hazy Images

Poonam Sharma¹,

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Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Let us enhance the quality of an image through a certain transformation function

T ()(\cdot)

. Now consider the WHE [42] technique which is applied over the greyscale image and it is given as in (2)–(4)

P_{w} ()(, G_{k}) = T (}{, P ()(, G_{k}))

P_{w} ()(, G_{k}) = ({, \begin{matrix} 1em4pt \\ \hat{P_{w}} ()(, G_{k}) P ()(, G_{k}) > τ_{1} \\ 0 P ()(, G_{k}) < τ_{2} \end{matrix})

τ_{1}

and

τ_{2}

are the upper and lower thresholds which are defined as

\begin{matrix} right leftthickmathspace.5em \\ τ_{1} \to β * max \{P (G_{k})\} \\ τ_{2} \to G_{k} < 1 \end{matrix}

where

β false\to

weight function and it takes a value

< 1

and not zero.…”

Section: Methodsmentioning

confidence: 99%

Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM

³

et al. 2020

IET Image Processing

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No abstract

“…Let us enhance the quality of an image through a certain transformation function

T ()(\cdot)

. Now consider the WHE [42] technique which is applied over the greyscale image and it is given as in (2)–(4)

P_{w} ()(, G_{k}) = T (}{, P ()(, G_{k}))

P_{w} ()(, G_{k}) = ({, \begin{matrix} 1em4pt \\ \hat{P_{w}} ()(, G_{k}) P ()(, G_{k}) > τ_{1} \\ 0 P ()(, G_{k}) < τ_{2} \end{matrix})

τ_{1}

and

τ_{2}

are the upper and lower thresholds which are defined as

\begin{matrix} right leftthickmathspace.5em \\ τ_{1} \to β * max \{P (G_{k})\} \\ τ_{2} \to G_{k} < 1 \end{matrix}

where

β false\to

weight function and it takes a value

< 1

and not zero.…”

Section: Methodsmentioning

confidence: 99%

Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM

³

et al. 2020

IET Image Processing

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No abstract

“…HE has a linear cumulative histogram that can distribute pixel values uniformly [11]. In another word, HE achieves a uniform distributed histogram by using the Cumulative density functions of the input image [12]. In contrast, "washed out" effect sometimes can be occurring to sink the quality of images.…”

mentioning

confidence: 99%

Image Enhancement Technique at Different Distance for Iris Recognition

¹

,

²

,

³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Capturing eye images within visible wavelength illumination in the non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques was tested against the different distance of eye image captured. Results were compared in terms of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on the different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at a four-meter distance was an ideal distance to capture eye images in a non-cooperative environment where it provides high recognition accuracy, 74%.

Contrast Enhancement of Medical Images Using Otsu’s Double Threshold

Vinay,

Agarwal,

Rani

et al. 2024

Lecture Notes in Networks and Systems

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No abstract

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