High efficient contrast enhancement using parametric approximation

Liu, Yun-Fu; Guo, Jing-Ming; Lai, Bo-Syun; Lee, Jiann-Der

doi:10.1109/icassp.2013.6638094

Cited by 22 publications

(9 citation statements)

References 17 publications

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“…A data structure called integral image [25] is used to speed up the calculation especially for local statistics. This idea was also proposed recently by Liu et al [26]. Liu et al used Gaussian as local parametric model.…”

Section: Introductionmentioning

confidence: 95%

Image enhancement using local intensity distribution equalization

Marukatat

2015

J Image Video Proc.

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This paper proposes a local intensity distribution equalization (LIDE) method for image enhancement. LIDE applies the idea of histogram equalization to parametric model in order to enhance an image using local information. It reduces the amount of computational resources required by traditional method like the adaptive histogram equalization, but allows enhancing detail similar to the latter technique. Integral image was used to efficiently estimate local statistics needed by the parametric model. This data structure drastically reduces the computational cost especially for megapixel image where a large local window is preferred. It should be noted that, with a large local window, the intensity distribution could contain multiple peaks. LIDE can nicely handle such complex distribution via mixture of parametric models. To speed-up the mixture parameter estimation, we propose an EM algorithm that is also based on the integral image data structure. Experimental results show that LIDE produces an enhanced image with greater detail and lower noise compared to several existing methods.

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Section: Introductionmentioning

confidence: 95%

Image enhancement using local intensity distribution equalization

Marukatat

2015

J Image Video Proc.

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“…CLAHE [16] denoises and enhances image contrast, but the noise filtering process removes some vein character information, which decreases image quality. Therefore, this study uses POHE [17] to contrast and enhance the image. POHE uses Gaussian parameters to construct the functional model for a Gaussian kernel and regional histogram equalization (HE) to enhance the image.…”

Section: Image Enhancement and Alexnetmentioning

confidence: 99%

A New Enhancement Edge Detection of Finger-Vein Identification for Carputer System

Hsia

Yang

Wang³

et al. 2022

Applied Sciences

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Developments in multimedia and mobile communication technologies and in mobilized, personalized information security has benefitted various sectors of society, as traditional identification technologies are often complicated. In response to the sharing economy and the intellectualization of automotive electronics, major automobile companies are using biometric recognition to enhance the safety, uniqueness, and convenience of their vehicles. This study uses a deep learning-based finger-vein identification system for carputer systems. The proposed enhancement edge detection adapts to the detected fingers’ rotational and translational movements and to interference from external light and other environmental factors. This study also determines the effect of preprocessing methods on the system’s effectiveness. The experimental results demonstrate that the proposed system allows more accurate identification of 99.1% and 98.1% in various environments, using the FV-USM and SDUMLA-HMT public datasets. As results, the contribution of system is high accuracy and stability for more sanitary, contactless applications makes it eminently suited for use during the COVID-19 pandemic.

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“…Image pre-processing has been reported in many papers as a fundamental step, particularly in those cases where the texture quality is non-favourable [26][27][28]. Different pre-processing algorithms are available in WELDMAP, including, among others: ACEBSF [29], POHE [30], RSWHE [31], Wallis [32], etc. This step is facultative but highly suggested in order to achieve better results in the feature extraction and matching step.…”

Section: Extraction and Matching Of Featuresmentioning

confidence: 99%

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

et al. 2022

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This paper presents a new tool for external quality control in welds using close-range photogrammetry. The main contribution of the developed approach is the automatic assessment of welds based on 3D photogrammetric models, enabling objective and accurate analyses through an in-house tool that was developed, WELDMAP. As a result, inspectors can perform external quality control of welds in a simple and efficient way without requiring visual inspections or external tools, and thus avoiding the subjectivity and imprecisions of the classical protocol. The tool was validated with a large dataset in laboratory tests as well as in real scenarios.

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High efficient contrast enhancement using parametric approximation

Cited by 22 publications

References 17 publications

Image enhancement using local intensity distribution equalization

Image enhancement using local intensity distribution equalization

A New Enhancement Edge Detection of Finger-Vein Identification for Carputer System

WELDMAP: A Photogrammetric Suite Applied to the Inspection of Welds

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