Comparison of Imaging Models for Spectral Unmixing in Oil Painting

Grillini, Federico; Thomas, Jean‐Baptiste; George, Sony

doi:10.3390/s21072471

Cited by 20 publications

(13 citation statements)

References 40 publications

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“…While, in [10], the authors deeply study the process of oil painting animation creation, analyze the relationship between animation creation and optimization in the information age from the source and characteristics of oil painting, and explain the value of animation created in the development stage of oil painting. Besides, the authors of [11] discussed the application of various mixing models in the study of oil painting, compared and analyzed the performance of different mixing models, and proposed evaluation protocols based on different characteristics, mainly including pigment mapping, spectral reconstruction, and concentration evaluation.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Oil Painting Formation Using Advanced Image Processing

Liu

2022

Mobile Information Systems

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The fast advancement of computer science and information technology has resulted in a significant revolution in the commercial painting sector. The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. With the rapid development and comprehensive popularization of computer technology, more and more people use computer image processing technology in oil painting created as a new way of creation. Some artists use a variety of ways to study the way of oil painting creation and use image processing technology to stimulate the creator’s imagination and improve the efficiency of drawing sketches in optimizing creative ideas, drawing, and processing original works. However, the value of art comes from creation. Oil painting creation based on computer image processing is a new type of artistic production that still requires the integration of different artists’ ideas and creative concepts. The image processing technology is a tool and a way for artists to make art while also realizing the convergence of science, technology, and art. This paper uses computer image processing technology to study oil painting creation. First, it briefly discusses computer image processing technology, focusing on picture preprocessing and image graying, and then uses this technique in formation of oil painting. Second, it establishes the oil painting mathematical, direction field, and color conversion models completing the oil painting image creation by using this model. Finally, the advantages of this technology are analyzed from the two aspects of oil painting creation, painting technique, and brush direction. The results show that 75% of artists are very satisfied with the digital painting technique, and 65% of artists are very satisfied with the painting brush direction, which shows that the application effect of computer image processing technology in oil painting creation is remarkable.

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Section: Related Workmentioning

confidence: 99%

“…is is done by calling L-alpha-beta spatial Gaussian matching, which assumes that the color distribution satisfies the characteristics of the Gaussian distribution. Equations (7) to (11) are the spatial conversion formula from RGB to L-alpha-beta.…”

Section: Color Conversion Modelmentioning

confidence: 99%

An Improved Oil Painting Formation Using Advanced Image Processing

Liu

2022

Mobile Information Systems

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“…This metric [ 81 ] is related to MSE (mean square error) [ 82 ], which directly compares two images through the mean of the pixel-by-pixel squared differences between the two images. Both PSNR and MSE are complementary metrics.…”

Section: Image Quality Metricsmentioning

confidence: 99%

Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range

Fernández-Carvelo

Martínez-Domingo

Valero

et al. 2021

Sensors

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Images captured under bad weather conditions (e.g., fog, haze, mist, dust, etc.), suffer from poor contrast and visibility, and color distortions. The severity of this degradation depends on the distance, the density of the atmospheric particles and the wavelength. We analyzed eight single image dehazing algorithms representative of different strategies and originally developed for RGB images, over a database of hazy spectral images in the visible range. We carried out a brute force search to find the optimum three wavelengths according to a new combined image quality metric. The optimal triplet of monochromatic bands depends on the dehazing algorithm used and, in most cases, the different bands are quite close to each other. According to our proposed combined metric, the best method is the artificial multiple exposure image fusion (AMEF). If all wavelengths within the range 450–720 nm are used to build a sRGB renderization of the imagaes, the two best-performing methods are AMEF and the contrast limited adaptive histogram equalization (CLAHE), with very similar quality of the dehazed images. Our results show that the performance of the algorithms critically depends on the signal balance and the information present in the three channels of the input image. The capture time can be considerably shortened, and the capture device simplified by using a triplet of bands instead of the full wavelength range for dehazing purposes, although the selection of the bands must be performed specifically for a given algorithm.

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“…This Painting dataset was firstly presented by Grillini et al [22]. In this dataset, seven different pigments are concerned: Kremer White, Ultramarine Blue, Naples Yellow, Carmine, Vermilion, Viridian Green, and Gold Ochre DD.…”

Section: Experiments On Multispectral Data Of Paintingmentioning

confidence: 99%

Extending the Unmixing methods to Multispectral Images

Cai¹,

Chatoux²,

Boust³

et al. 2021

cic

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In the past few decades, there has been intensive research concerning the Unmixing of hyperspectral images. Some methods such as NMF, VCA, and N-FINDR have become standards since they show robustness in dealing with the unmixing of hyperspectral images. However, the research concerning the unmixing of multispectral images is relatively scarce. Thus, we extend some unmixing methods to the multispectral images. In this paper, we have created two simulated multispectral datasets from two hyperspectral datasets whose ground truths are given. Then we apply the unmixing methods (VCA, NMF, N-FINDR) to these two datasets. By comparing and analyzing the results, we have been able to demonstrate some interesting result for the utilization of VCA, NMF, and N-FINDR with multispectral datasets. Besides, this also demonstrates the possibilities in extending these unmixing methods to the field of multispectral imaging.

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Comparison of Imaging Models for Spectral Unmixing in Oil Painting

Cited by 20 publications

References 40 publications

An Improved Oil Painting Formation Using Advanced Image Processing

An Improved Oil Painting Formation Using Advanced Image Processing

Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range

Extending the Unmixing methods to Multispectral Images

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