Adaptive clip-limit-based bi-histogram equalization algorithm for infrared image enhancement

Paul, Abhisek; Sutradhar, Tandra; Bhattacharya, Prabir; Maity, Santi P.

doi:10.1364/ao.395848

Cited by 13 publications

(3 citation statements)

References 47 publications

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“…In image processing, general arithmetic operations are not suitable for some actual image processing work, and the result obtained by directly adding (or multiplying) two images has a certain gap with the human visual effect, and will produce "hyper-interval value" problem, the LIP model provides a new arithmetic construct that defines new vector operations such as addition, subtraction, multiplication, etc. The gray values of the images applied with this model are all in the (0, M) interval, so as to avoid the problem of exceeding the interval value, which is also consistent with the saturation characteristics of the human visual system [11].…”

Section: B Weak Signal Enhancement Of Low-light Images Based On Multi...mentioning

confidence: 60%

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

Yin¹,

Li²

2022

IJACSA

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There is noise interference in low-illumination images, which makes it difficult to extract weak signals. For this reason, this paper proposes a low-illumination image weak signal enhancement algorithm based on neural network. Multi-scale normalization is performed on low-light images, and multi-scale Retinex is used to enhance weak signals in low-light images. On this basis, the GAN artificial neural network is used to detect the weak signal of the weak signal in the image, the normalization of the weak signal of the low-illumination image is completed based on the residual network, the self-encoding parameters of the depth residual are generated, and the weak signal enhancement result of the low-illumination image is output. The experimental results show that the method in this paper has better enhancement effect on low-illumination images and better image denoising effect. When the scale value is large, the low-contrast area of the low-illumination image has a better enhancement effect. The saturated area of the low-light image has a better enhancement effect.

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Section: B Weak Signal Enhancement Of Low-light Images Based On Multi...mentioning

confidence: 60%

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

Yin¹,

Li²

2022

IJACSA

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“…Therefore, this article proposes a joint training of local alignment submodules and feature based editing submodules to solve such problems. The literature proposes a platform value adaptive ADPHE algorithm that utilizes gray and local maximum prediction to calculate the value of the platform in all situations, enabling the algorithm to adapt to the situation and achieve adaptive selection and real-time updates of platform values (Paul et al 2020). However, the ADPHE algorithm is still not incremental.…”

Section: Related Workmentioning

confidence: 99%

Application of Interactive Artificial Intelligence System Based on Image Detail Enhancement Algorithm in Clothing Graphic Design

2024

Preprint

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With the continuous improvement of the aesthetic quality of the modern society, the demand for fashion is rapidly growing, and more and more users are beginning to experience the independent design and customization of fashion clothing. The development and progress of digital and intelligent technologies provide opportunities to address the challenges of interactive artificial intelligence in clothing graphic design processes. Therefore, this article studies interactive artificial intelligence systems using image detail enhancement algorithms and analyzes their application in clothing graphic design. Firstly, using adaptive configuration and detail enhancement algorithms to improve image quality and improve the matching effect of feature points, and conducting experiments on the improvement index and global contrast index through evaluation indicators to examine the feasibility of the algorithm proposed in this paper; Secondly, the clothing graphic design algorithm was studied, and the feature database samples were tested using the clothing graphic design algorithm to demonstrate the relationship between classification accuracy and the number of cycles; Finally, construct an interactive artificial intelligence system, study the application of interactive artificial intelligence systems in clothing graphic design, and propose a clothing pattern communication platform. Further proves that interactive artificial intelligence systems based on image detail enhancement algorithms have high application value in clothing graphic design.

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“…However, the major drawback of this method is losing the originality of image, loss of image information, over enhancement of brightness as well as the contrast and amplified the noise from the original image. Many attempts done by researchers to reduce the HE drawback by introducing several methods such as adaptive histogram equalization (AHE) [12], contrast limited adaptive histogram equalization (CLAHE) [13], brightness preserving bi-histogram equalization [14], sub-image histogram equalization method [15], recursive mean separate histogram equalization [16] and bi-histogram equalization [17].…”

mentioning

confidence: 99%

Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images

2023

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Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images' original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively.

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Adaptive clip-limit-based bi-histogram equalization algorithm for infrared image enhancement

Cited by 13 publications

References 47 publications

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

A Neural Network-Based Algorithm for Weak Signal Enhancement in Low Illumination Images

Application of Interactive Artificial Intelligence System Based on Image Detail Enhancement Algorithm in Clothing Graphic Design

Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images

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