Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement

Sengupta, Debapriya; Biswas, Arindam; Gupta, Phalguni

doi:10.1007/s11042-020-09583-1

Cited by 21 publications

(6 citation statements)

References 51 publications

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“…Gamma correction is used to improve the contrast of the panoramic image, and the shadow area of the image edge still needs further processing. e edge of the panoramic image is extracted by band-pass filter and Gaussian difference filter [21,22], so as to reduce the influence of illumination intensity on the edge region of the panoramic image. e panoramic image I (x, y) is filtered by a Gaussian difference filter, also, the equation is as per the following:…”

Section: Brightness Normalizationmentioning

confidence: 99%

Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

Kang

2022

Mathematical Problems in Engineering

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A panoramic image texture detail enhancement method based on the Retinex algorithm is proposed to work on the nature of panoramic images. Firstly, the panoramic image is collected, then the panoramic image is preprocessed through brightness enhancement, the brightness of the preprocessed panoramic image is normalized, and the panoramic image is optimized and increased by the improved Retinex algorithm. Finally, the simulation test of the panoramic image was carried out. The results show that the improved Retinex algorithm works on the sign to-commotion proportion of the panoramic image. Furthermore, the time required to enhance the panoramic texture detail is short, which can meet the practical requirements of panoramic image subsequent processing.

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Section: Brightness Normalizationmentioning

confidence: 99%

Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

Kang

2022

Mathematical Problems in Engineering

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“…We can rewrite MQP and all the PQPs, PQPyaw, PQPpitch and PQProll in their color versions, by replacing their value component of the HSV color space. The color versions, CMQP and all the CPQPs, CPQPyaw, CPQPpitch and CPQProll, are written in the following form: 𝐶𝑀𝑄𝑃 𝑛,𝑚 = 𝑀𝑄𝑃 𝑛,𝑚 + 𝑟 , 𝒊 + 𝑔 , 𝒋 + 𝑏 , 𝒌 (15) 𝐶𝑃𝑄𝑃 𝑦𝑎𝑤,𝑛,𝑚 = 𝑃𝑄𝑃 𝑦𝑎𝑤,𝑛,𝑚 + 𝑟 , 𝒊 + 𝑔 , 𝒋 + 𝑏 , 𝒌 (16) 𝐶𝑃𝑄𝑃 𝑝𝑖𝑡𝑐ℎ,𝑛,𝑚 = 𝑃𝑄𝑃 𝑝𝑖𝑡𝑐ℎ,𝑛,𝑚 + 𝑟 , 𝒊 + 𝑔 , 𝒋 + 𝑏 , 𝒌 (17) 𝐶𝑃𝑄𝑃 𝑟𝑜𝑙𝑙,𝑛,𝑚 = 𝑃𝑄𝑃 𝑟𝑜𝑙𝑙,𝑛,𝑚 + 𝑟 , 𝒊 + 𝑔 , 𝒋 + 𝑏 , 𝒌 (18) The CMQP and all the CPQPs, CPQPyaw, CPQPpitch and CPQProll, together are called CQ-MPP. The visualizations of CMQP and CPQPs are shown in the on the second to fourth rows of figure 2.…”

Section: Color Quaternion Modulus and Phase Patterns (Cq-mpp)mentioning

confidence: 99%

“…The top rows of figure 4 show various dimmed versions of the images in figure 1. These dimmed images are synthetically generated using a combination of logarithm gamma correction function (LGCF) [16]. These images are obtained by converting to greytone images then applying LGCF on the greytone images.…”

Section: Contrast Remapping Functionmentioning

confidence: 99%

Enhancing the resilience of wireless capsule endoscopy imaging against adversarial contrast reduction using color quaternion modulus and phase patterns

Liew,

Agaian,

Zhao

2024

Multimodal Image Exploitation and Learning 2024

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Wireless capsule endoscopy (WCE) offers a minimally invasive approach to inspecting the gastrointestinal (GI) tract, crucial for diagnosing conditions such as malnutrition, dehydration, and potential cancers. However, WCE image diagnostics can be compromised by inadequate illumination and adversarial contrast reduction attacks. Adversarial contrast reduction attacks are intentional efforts to degrade image contrast and mislead automated diagnosis systems. Such challenges can result in misclassifications, negatively impacting patient safety.This study examines the effects of contrast degradation on Deep Learning (DL) models designed for WCE image analysis. The study emphasizes the adverse impact of substantial contrast reductions from adversarial attacks on classification accuracy. We propose a novel texture descriptor to mitigate this vulnerability: the Color Quaternion Modulus and Phase Patterns (CQ-MPP). This descriptor effectively extracts textural features within WCE images, enabling the identification of potentially cancerous regions, even under significantly reduced contrast. The effectiveness of CQ-MPP is evaluated using the Wireless Capsule Endoscopy Curated Colon Disease Dataset. Results show that CQ-MPP maintains good accuracy in detecting cancerous lesions and demonstrates remarkable resilience to contrast adversarial degradation. This method ensures reliable performance amidst severe contrast reduction, offering significant potential to improve safety of GI disease diagnosis via WCE.

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“…In the proposed method, a multi-scale Gaussian function is used to extract images with uneven brightness components, and the adaptive gamma function [29,30] is adjusted to an adaptive brightness gamma function correction function. Its purpose is to effectively correct the uneven illumination image, while retaining the effective information of the original image, thereby enhancing the visual effect of the image.…”

Section: Adaptive Gamma Correctionmentioning

confidence: 99%

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

Yan

Yuan

et al. 2023

Preprint

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Aiming at the problems of blurring and distortion of traditional patterns, an enhancement method to improve resolution and contrast is proposed.First, the discrete wavelet transform, stationary wavelet transform, and interpolation algorithm are used to obtain high-resolution images of traditional patterns. Then, the improved singular value matrix coefficients and the reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. The experimental results show that the evaluation indexes such as mean square error, peak signal-to-noise ratio, and structural similarity corresponding to this method are improved compared with other comparison methods. It can effectively improve the quality of traditional patterns and is of great significance to the research on the restoration and protection of traditional patterns.

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Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement

Cited by 21 publications

References 51 publications

Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

Retinex Algorithm and Mathematical Methods Based Texture Detail Enhancement Method for Panoramic Images

Enhancing the resilience of wireless capsule endoscopy imaging against adversarial contrast reduction using color quaternion modulus and phase patterns

Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

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