A Meta-Analysis of Contrast Measures Used for the Performance Evaluation of Histogram Equalization Based Image Enhancement Techniques

Anoop, B N; Ameenudeen, P E; Joseph, Justine

doi:10.1109/icccnt.2018.8494069

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Different image quality metrics were proposed for assessment of methods for CE, such as absolute mean brightness error (AMBE), 20 discrete entropy, 12 measure of enhancement (EME) and EME based on entropy (EMEE), 65 QRCM, 66 UIQ, 67 EBCM, 68 and CII 29 . A more detailed overview of further image quality metrics and methods for CE can be found in another work 69 …”

Section: Resultsmentioning

confidence: 99%

“…In Fig. 13, values for nonreference image quality metrics EBCM, 68 EME, EMEE, 65 and entropy 12 over SNRbackground are shown.…”

Section: Resultsmentioning

confidence: 99%

“…Means of nonreference metric values Q0 (EBCM, 68 EME, 65 EMEE, 65 and entropy, 12 left column) and metric differences ΔQCE=QCE−Q0 for three CE methods, i.e., CLAHE, 13 EHS, 17 and SUACE 28 (right columns) over Nbackground=1000 images of 64×64 pixels with a centered triangle with circumradius r=10 pixel for different SNRbackground(dB) and SNRnoise (dB). ΔQCE=0 is shown as a horizontal blue dashed line.…”

Section: Resultsmentioning

confidence: 99%

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Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Wegner

Keßler

2023

Opt. Eng.

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Within the last decades, a large number of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks, such as object recognition. In this work, the effect of contrast enhancement algorithms is evaluated by means of the triangle orientation discrimination (TOD), which is a current method for imager performance assessment. The conventional TOD approach requires observers to recognize equilateral triangles pointing in four different directions, whereas here convolutional neural network models are used for the classification task. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed over natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then, these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms, such as the contrast-limited adaptive histogram equalization or local range modification, are applied. Then accuracies of the trained models on these images are compared for different contrast enhancement algorithms. Accuracy gains for low signal-to-background ratios and sufficiently large triangles are found, whereas impairments are found for high signal-to-background ratios and small triangles. A high generalization ability of our TOD model is found from the similar accuracies for several image databases used for backgrounds. Finally, implications of replacing triangles with real target signatures when using such advanced digital signal processing algorithms are discussed. The results are a step toward the assessment of those algorithms for generic target recognition.

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Section: Resultsmentioning

confidence: 99%

“…In Fig. 13, values for nonreference image quality metrics EBCM, 68 EME, EMEE, 65 and entropy 12 over SNRbackground are shown.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Wegner

Keßler

2023

Opt. Eng.

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“…The HE has gained much popularity among these techniques, which culminated into incursion of different variants of it in the image processing literature. Moreover, a meta-analysis of some widely used contrast measures for assessment of image enhancement schemes that are based on the HE is provided in [10].…”

Section: Introductionmentioning

confidence: 99%

Optimised enhancement scheme for low contrast underwater images

et al. 2020

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Images acquired in underwater environments are usually affected by light absorption and scattering. These are the two phenomena that reduce the clarity of images that are captured in these environments. These factors cause low contrast and anamorphic colour diffusion. To tackle these issues, we propose an optimized low contrast enhancement scheme. The main thrust of this paper borders on enhancement of underwater image contrast by preserving the brightness level. The approach is termed Fuzzy-Histogram Equalisation Optimised for Brightness Preservation (FHEOBP) technique, where a combination of fuzzy and classical histogram equalisation techniques is employed towards the enhancement of the contrast of images from underwater scene. The scheme is optimized using teaching-learning-based optimisation technique that is built into the algorithm. The proposed FHEOBP filter shows improved performance over Local Histogram Equalisation (LHE) and Global Histogram Equalisation (GHE) as it has a higher luminance distortion index value than those of LHE and GHE. This translates into a better image details preservation. In fact, the computed luminance distortion indices for optimised FHEOBP are 16.4%, 28.3% and 20.1%, respectively higher than those of the corresponding GHE, in the same test images utilised for performance evaluation. Between the optimised and non-optimised FHEOBP, luminance distortion figures of optimised FHEOBP are 8%, 6.8% and 9.8% higher than those of the equivalent non-optimised FHEOBP in the test image data set.

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Parameter-free fuzzy histogram equalisation with illumination preserving characteristics dedicated for contrast enhancement of magnetic resonance images

Renuka

Edla

Joseph

et al. 2020

Applied Soft Computing

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A Meta-Analysis of Contrast Measures Used for the Performance Evaluation of Histogram Equalization Based Image Enhancement Techniques

Cited by 6 publications

References 22 publications

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Optimised enhancement scheme for low contrast underwater images

Parameter-free fuzzy histogram equalisation with illumination preserving characteristics dedicated for contrast enhancement of magnetic resonance images

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