Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

Liu, Qiang; Huang, Zhengdong; Pointer, Michael R.; Luo, Ming Ronnier

doi:10.3390/app9245308

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…The h sample clustering subspaces C h , h centroids are obtained by equations ( 4) and (5). The similarity distance between each testing sample point p i and the centroids of the h clustering subspaces is calculated one by one using equation ( 4).…”

Section: K-mean Angle Similar Clusteringmentioning

confidence: 99%

“…Because of its uniqueness, spectral reflectance is called the 'fingerprint' of an object. Spectral reflectance is therefore widely used in the fields of artwork high-fidelity reproduction [3][4][5], telemedicine [6][7][8], textiles [9,10], remote sensing [11,12], and other fields [13,14]. The spectral reflectance can be obtained directly in the laboratory by using spectrophotometer measurements, which requires direct contact with the surface of the object.…”

Section: Introductionmentioning

confidence: 99%

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Optimized clustering sample selection for spectral reflectance recovery

Wu,

Niu,

Xiong

2023

Laser Phys. Lett.

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The accuracy of spectral recovery depends heavily on the selection of an appropriate sample set, so the optimized sample selection by clustering strategy can improve the spectral recovery results. This paper presents a sample optimization method that combines hierarchical clustering and K-mean angle similar clustering to achieve this process. The proposed method employs the hierarchical clustering to divide the training sample dataset into 15 subspaces and obtain 15 subspace centroids. The similarity distance is then calculated between the testing sample and each subspace samples, and the subspace with the sample having the smallest distance is selected. The testing sample is utilized as a priori centroid, which clusters the optimal subspace by competition with the centroid of the subspace selected. This iterative process continues until the centroid of the subspace remains unaltered. Finally, the training samples within the optimal subspace use to recover spectral reflectance through Euclidean distance weighting. Experimental results demonstrate that the proposed method outperforms existing methods in terms of spectral and colorimetric accuracy, as well as stability and robustness. This research provides a solution to the problem of data redundancy in the spectral recovery process and enhances the accuracy and efficiency of spectral recovery.

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Section: K-mean Angle Similar Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized clustering sample selection for spectral reflectance recovery

Wu,

Niu,

Xiong

2023

Laser Phys. Lett.

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“…Spectral reflectance is an inherent nature of matter itself, which can be regarded as the ‘fingerprint’ of an object [ 1 ]. Due to its unique properties, it can be used to characterize the color of materials and the surface properties of materials in art [ 2 , 3 , 4 ], remote sensing [ 5 , 6 ], medicine [ 7 , 8 , 9 ], textiles [ 7 , 10 ] and so on [ 11 , 12 ]. In real life, it is difficult to directly obtain the surface spectral reflectance of an object; researchers mostly obtain the surface spectral reflectance by indirectly obtaining the response value of a digital camera [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Xiong

2023

Sensors

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The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples.

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“…Spectral reflectance describes wavelength-dependent reflectance functions and determines the intrinsic color characteristics of object materials ( Liang and Wan, 2017 ). Spectral matching can successfully predict the color appearance of a scene under arbitrary environmental conditions, which has led to its widespread use in high-fidelity reproduction ( Liu et al, 2019 ), remote sensing ( Lin and Finlayson, 2020 ), cultural heritage ( Liang, 2011 ), medical diagnosis ( Depeursinge et al, 2009 ; Nishidate et al, 2013 ; Xiao et al, 2016 ), etc. ( Wu et al, 2016 ; Jinxing et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Optimized clustering method for spectral reflectance recovery

Xiong

et al. 2022

Front. Psychol.

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An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as a priori clustering center to obtain the clustering subspace by competition. The Euclidean distance weighted and polynomial expansion models in the clustering subspace were adaptively applied to improve the accuracy of spectral recovery. The experimental results demonstrated that the proposed method outperformed existing methods in spectral and colorimetric accuracy and presented the effectiveness and robustness of spectral recovery accuracy under different color spaces.

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Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

Cited by 7 publications

References 33 publications

Optimized clustering sample selection for spectral reflectance recovery

Optimized clustering sample selection for spectral reflectance recovery

Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery

Optimized clustering method for spectral reflectance recovery

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