Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images

Huang, Zhenghua; Zhang, Tianxu; Li, Qian; Hao, Fang

doi:10.1016/j.infrared.2016.11.001

Cited by 66 publications

(32 citation statements)

References 27 publications

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“…Therefore, in this study, the structure was distinguished from the background by performing histogram transformation to measure the response of the tubular level gauge. For the histogram transformation, histogram stretching [38,39] and gamma correction [40,41] of Equation 5were applied. Equation 5 When γ is 1, identity transformation is performed.…”

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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“…When γ values are set above 1, the bright input pixel values are transformed to a narrow range of dark output values. Further details on GC method can be found in Huang et al (2016). In this study, five potential γ values (0.1, 0.3, 0.5, 0.7, and 0.9) were tested.…”

Section: Gamma Correction (Gc)mentioning

confidence: 99%

“…Numerous image enhancement methods such as Histogram Equalization (Stark, 2000), Linear Contrast Stretching (Gillespie, 1992), Brightness Preserving Bi-Histogram Equalization (Moniruzzaman et al, 2014), Local Histogram Equalization (Kim et al, 1998), Gamma Correction (Huang et al, 2016), Adaptive Histogram Equalization (Pizer et al, 1987), Contrast Limited Adaptive Histogram Equalization (Pizer et al 1990), Minimum Mean Brightness Error Bi-histogram Equalization (Chen, Ramli, 2003), Dualistic Sub-image Histogram Equalization (Wang et al, 1999) are available for processing low visual quality images. These methods use either spatial or frequency domain for enhancing the quality of the images.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Enhancement Techniques for Rapid Processing of Post Flood Images

Harichandana

Sowmya

Variyar

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.

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“…For quantitative assessment of contrast enhancement methods, four parameters namely discrete entropy [23], measure of enhancement (EME) [24], Mean Structural Similarity Index Measurement (MSSIM) [25] and Feature Similarity Index Measurement (FSIM) [26] are considered in this paper.…”

Section: Quantitative Assessmentmentioning

confidence: 99%

Hybrid bi-dimensional empirical mode decomposition based enhancement technique for extreme low contrast UAV thermal images

Thillainayagi

Kumar

2019

Sādhanā

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The performance of automatic target detection and classification systems are typically affected by reduced contrast quality introduced by external interferences. In particular for Unmanned Aerial Vehicle (UAV) captured thermal surveillance images, the effect is more evident. This advises the use of contrast enhancement technique as a solution to enhance the reduced contrast of hot regions for efficient target detection. In this paper, a simple and novel enhancement technique based on singular value decomposition (SVD) using Bi-Dimensional Empirical Mode Decomposition (BEMD) is proposed to enhance the hot regions in extreme low contrast thermal images captured by UAV. In the first step, the technique decomposes the thermal image into Intrinsic Mode Functions (IMFs) and residue by using BEMD. In the second step, it applies Contrast Limited Adaptive Histogram Equalization (CLAHE) in the residue for local contrast enhancement and then calculates the singular value matrix. In the third step, residue component is rescaled for further improvement of hot regions using scaling factor. In the fourth step, a detail enhanced IMF components are generated using gray scale transformation. Finally, the contrast enhanced residue undergoes Inverse BEMD (IBEMD) together with the detailed enhanced IMFs for enhanced image generation. Experimental results demonstrate that the proposed technique effectively enhances the contrast and details in the image with less visual artefacts than other state-of-the-art techniques.

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Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images

Cited by 66 publications

References 27 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

Comparison of Image Enhancement Techniques for Rapid Processing of Post Flood Images

Hybrid bi-dimensional empirical mode decomposition based enhancement technique for extreme low contrast UAV thermal images

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