Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

Han, Zhenghao; Li, Li; Jin, Weiqi; Wang, Xia; Jiao, Gangcheng; Wang, Hailin

doi:10.1109/jphot.2020.3025088

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…On this basis, it analyzes the color shift of the color, in the color of the number, the human eye is more sensitive to the neutral gray part of the gray, because in the gray [16], as long as there is a certain amount of color, it will be found by people's eyes. Therefore, the gray balance detection algorithm is used to check the offset in the image.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Hu¹,

Hou

Song³

2022

Wireless Communications and Mobile Computing

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In order to improve the matching effect of environmental art color automatic matching, a model of environmental art color automatic matching based on visual matching is designed. Firstly, it preprocesses the color matching of environmental art, then extracts the color space and its main color, and analyzes the color deviation. At the same time, the color model is established and the color quantization is processed, and finally the color matching is realized. The experimental results show that the matching time of the proposed model is less than that of the traditional model, and the wrong contour is less. Research Objective. A model of environmental art color-automated matching based on visual matching is developed in order to improve the matching effect of environmental art color matching. Current Challenges. Because of time constraints, the matching model still has certain drawbacks, necessitating more optimization research in the subsequent study. The main steps in the research are pretreatment before color matching of environmental art, color space and main color extraction, color deviation analysis, color modeling, color quantization processing, implementation of color matching, and experimental comparison.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Hu¹,

Hou

Song³

2022

Wireless Communications and Mobile Computing

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“…e detection results of real images show that the neural network can detect a variety of colors, which has a certain auxiliary role in color language analysis [16]. Han et al proposed using convolution neural network to complete the fitting of bias color and real color, improve the camera's shooting quality of painting works, and ensure the authenticity of the color language of works [17]. Liu et al proposed using BP neural network to segment retinal vessels in color fundus images, which shows that BP neural network has application value in color analysis [18].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Color Language and Aesthetic Paradigm of Print Art Based on GB‐BP Neural Network

Chen

Tan

2021

Computational Intelligence and Neuroscience

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Color is the basic element of printmaking art creation and also an important medium for artists to express their emotions. In order to improve the understanding of the print by tourists, the research first carries out image analysis of different colors, summarizes the common color image adjectives in the art of publishing and painting, then optimizes BP neural network algorithm by gradient promotion (GP) algorithm, and constructs a color language analysis technology based on GP-BP neural network review paradigm. Through this technology, the content of color expression of different types of print art works is analyzed. Then, the subjective evaluation of these print art works is carried out by questionnaire adjustment method. Finally, the subjective evaluation and network evaluation results are compared. On the three materials, the expression effect of color image adjectives such as “heavy light fast,” “modern classical,” “lively steady,” and “soft Yang Gang” is consistent ( P value is greater than 0.05). The first set of print works mainly reflects the artist’s “affinity cold” emotion. The MSE values of the evaluation results are less than 0.01. That is, the color language analysis technology of print art based on GP-BP neural network can reflect the artist’s emotion to a certain extent when analyzing the color language of the print.

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“…The next step is validation [4], Trials [5], and Revision [6]. When planning and carrying out tests to develop adaptive algorithms to improve the quality of license plate images based on lighting parameters [21,22,23,24,25], this is done in stages to produce output according to plan and supporting theory. There are two phases in design and engineering, i.e., hardware and software design.…”

Section: Fig1 Research Stages Workflowmentioning

confidence: 99%

Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates based on Lighting Parameters

Suhartono,

Suhardi,

Angraini

2023

IOTA

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This study aims to develop an Adaptive Algorithm to Improve the Image Quality of Vehicle Number Plates Based on Lighting Parameters. The type of research used by the Author is Research and Development. This research was conducted at the Computer Engineering Laboratory for six months. This research consists of several stages, from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The collected datasets were taken using smartphone cameras and webcams, with 207 image datasets divided into two categories: training data and validation. The training dataset of 207 objects was 100% successful. System testing was carried out in two conditions, namely during the day and at night, for each two-wheeled and four-wheeled vehicle object. The results of adaptive algorithm testing to improve the quality of vehicle license plate images based on the light parameter experienced a change in the average MSE and PSNR values between the original image and the quality-improved image, although not too much of a difference. Based on this, it can be interpreted that the adaptive algorithm can produce better photos than before.

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Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

Cited by 11 publications

References 26 publications

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

[Retracted] Analysis of Color Language and Aesthetic Paradigm of Print Art Based on GB‐BP Neural Network

Adaptive Algorithm to Improve the Image Quality of Vehicle License Plates based on Lighting Parameters

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