Adaptive contrast enhancement using edge-based lighting condition estimation

Jang, Chan Young; Kang, Suk‐Ju; Kim, Young Hwan

doi:10.1016/j.dsp.2016.04.009

Cited by 7 publications

(2 citation statements)

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“…CII (contrast improvement index) gives the quantitative contrast improvement ratio between input and reconstructed images using edge-based contrast measurement 41 . Much higher and very low ratios are clearly worse.…”

Section: Experimental Results With Discussionmentioning

confidence: 99%

“…CII (contrast improvement index) gives the quantitative contrast improvement ratio between input and reconstructed images using edge-based contrast measurement. 41 Much higher and very low ratios are clearly worse. Nevertheless, a tiny increase of CII in percentage is better while keeping absolute mean brightness error (AMBE) and MSE values smaller.…”

Section: Image Quality Measurementsmentioning

confidence: 99%

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Iterative histogram equalization using discrete wavelet transform in low-dynamic range

Bulut

Ince

2023

J. Electron. Imag.

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.A progressive histogram equalization (HE) and contrast enhancement method is proposed in the frequency domain. This method has an iterative structure based on the 6-sigma rule in the low-dynamic range using discrete wavelet transform. The proposed method determines an automatic threshold for attenuating the high-frequency amplitude of a signal in the discrete wavelet domain, based on the standard deviation of the absolute power of the high-frequency components in the signal band generated from the probability density function (PDF). Then an iterative quantization procedure is used where the PDF values in the frequency domain are randomly quantized. Besides, the maximum Bhattacharyya coefficient, which matches the original input image’s PDF, is used as a cost function to optimize the histogram smoothing output. For the random number generation, a 32-bit pseudorandom generator is employed to produce the same result for the output image at each runtime. The quantization factor parameter enables a controlled contrast enhancement rate to receive balanced equalization results in images. In the experimental studies, the proposed iterative HE method is compared with the well-known global, local, and brightness preserving algorithms. Experimental studies quantitatively and qualitatively display promising and encouraging results in terms of various state-of-the-art quality assessment metrics such as mean squared error, peak signal-to-noise ratio, structural similarity index measurement, contrast improvement index, absolute mean brightness error, and quality-aware relative contrast measure.

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Section: Experimental Results With Discussionmentioning

confidence: 99%

Section: Image Quality Measurementsmentioning

confidence: 99%