Variational infrared image enhancement based on adaptive dual-threshold gradient field equalization

Zhao, Wenda; Xu, Zhenbang; Zhao, Jian; Zhao, Fan; Han, Xizhen

doi:10.1016/j.infrared.2014.05.018

Cited by 22 publications

(11 citation statements)

References 17 publications

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“…Moreover the HVS is very sensitive to intensity changes (gradient) rather than the absolute image intensities. Hence variational infrared image enhancement algorithm based on gradient field equalization [36] was introduced to enhance the gradients specifically with adaptive dual thresholds. The gradients of each pixel in the image form a vector field named as gradient field.…”

Section: Direct Contrast Enhancement Techniquesmentioning

confidence: 99%

“…Histogram equalization just tends only to enhance the image (high probability details) whereas Gradient Histogram Equalization enhances the fine edge details of the image. Hence the concept Gradient histogram equalization [36], [42] was utilized in most of the research work. Most of these image enhancement methods are not robust as each approach is geared to handle images degraded at particular level types.…”

Section: Comparisons Between the Techniquesmentioning

confidence: 99%

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Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Supriya¹,

Subaji²

2017

IJSIP

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Section: Direct Contrast Enhancement Techniquesmentioning

confidence: 99%

Section: Comparisons Between the Techniquesmentioning

confidence: 99%

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Supriya¹,

Subaji²

2017

IJSIP

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“…Histograms of gradients basically provide information about the occurrence of gradient orientations in a localized region of the images. Hence, they are able to characterize shapes in regions of concern (Santhi and Wahida Banu, 2015;Randa and Rabab Farouk, 2015;Lidong et al, 2015;Honglie et al, 2015;Kuldeep et al, 2015;Xiangzhi, 2015;Wenda et al, 2014;Jing and Nor Ashidi, 2014;Mohammad Farhan et al, 2014b;2014a;Kuldeep and Rajiv, 2014a;Chien-Cheng et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Composite Structure Surface Uniformity using Interval Based Gradient Field Histogram Analysis of Thermographic Images (IGF-HA)

Iskandarani¹

2018

Journal of Computer Science

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This paper presents a new approach to composite surface characterization using Image Histogram Analysis as a function of Gradient Field. Image sequences are weighed and combined in order to present a pattern change in the thermal response of a tested composite structure. Reaction Injection Molding samples used and subjected to thermal energy to characterize their surface uniformity and any existing damage. A threshold value is used for the purpose of segmenting and separating damaged from undamaged areas in the tested composite structure. Gradient field analysis established the critical time at which the surface started to show damage and segmented the tested images into areas of concern. The resulting histograms cover four main regions of interest according to the gradient intensities as a function of time. Each region is divided into eight subsections according to the corresponding limit value, resulting in thirty two subsections. Correlation between region gradient field and its histogram resulted in uncovering of surface deformities as a function of surface area thermal storage. The process is modeled mathematically.

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“…With the maturity of the surface roughness detection technology, all the thermal infrared imagers today have extremely high dynamic range (HDR), which can reach up to 14 bits or even higher (in this paper, 14 bits is adopted as an example) [1,2]. The relatively high dynamic range ensures that the thermal imager can still clearly distinguish the details of the temperature change in the scene where the temperature change is extremely great [3][4][5]. However, high dynamic range can cause a mismatching issue between the original image captured and the back-end device [6,7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive image enhancement algorithm based on the model of surface roughness detection system

Tian

Yin

2018

J Image Video Proc.

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In view of the relatively high noise interference and halo phenomenon of the traditional adaptive image enhancement algorithm based on the unsharp masking method, a kind of adaptive image enhancement algorithm based on the integration of the model of surface roughness detection system (hereinafter referred to as MSRDS for short) is put forward in this paper. Through the design of the model of the surface roughness detection system, non-linear segmentation, denoising, and adaptive amplification are carried out on the details of the image under this system model. The dynamic range compressed image base layer and the adaptively enhanced image detail layer are non-linearly superimposed to obtain the final enhanced image. Finally, through the comparative experiment analysis, it demonstrates that the method put forward in this paper can suppress the interference noise and the halo phenomenon of the image very well while carrying out dynamic range compression and detail amplification of the adaptive image effectively. And the result thus obtained is very suitable for the back-end image processing of the actual thermal infrared imager.

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Variational infrared image enhancement based on adaptive dual-threshold gradient field equalization

Cited by 22 publications

References 17 publications

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Intelligent Based Image Enhancement using Direct and In-Direct Contrast Enhancement Techniques – A Comparative Survey

Characterization of Composite Structure Surface Uniformity using Interval Based Gradient Field Histogram Analysis of Thermographic Images (IGF-HA)

Adaptive image enhancement algorithm based on the model of surface roughness detection system

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