Conversely, self-luminous color refers to the color created by the devices’ own lights, such as Liquid Crystal (LC) Display, Organic Light Emitting Diode (OLED), and CRT displays. Surface color refers to the color that is created by a reflected light that bounces off an object, such as colors on paper, wood, and fabric. Our results can be considered to be of great significance not only for color science, but also for the color industry.Ĭolors are broadly classified as surface and self-luminous colors. We experimentally demonstrated that there is no essential problem in cross-media color matching by generating isomeric pairs.
However, metameric color matching fails for most participants, indicating that differences in the spectral distributions rather than the different color-generating mechanisms themselves are the reason for the color matching failure between different devices. The results show that isomeric color matching between self-luminous and surface colors can be performed for all participants. Then, we experimentally verified whether such color matching can be performed. In the present study, we created isomeric color-matching pairs between self-luminous and surface colors by modulating the spectral distribution of the light for surface colors. However, the reason for this failure remains unclear. In fact, previous research has reported that metameric color matching fails among diverse media. These are completely different mechanisms, and thus, surface color and self-luminous color cannot be matched even though both have identical tristimulus values. By contrast, self-luminous color results directly from an emitting light, such as a Liquid Crystal (LC) display. The results show that the effect of reconstruction is consistent with the original image of the flame, and the geometric characteristics of the flame are accurately reconstructed.Surface color results from a reflected light bounced off a material, such as a paper.
A number of experiments as well as subjective and objective analyses were subsequently carried out on this dataset. The proposed method was tested through a supersonic combustion experiment in a ground wind tunnel under different equivalence ratios, and data on the flow field of the combustion chamber and the evolution of the flame were recorded as a dataset. The channel and spatial attention mechanisms enable the model to focus on important feature-related information, and the residual connection prevents gradient disappearance to improve the capability of the model for generalization. We compare the reconstruction-related performance of single-path and dual-path models under different conditions. In this study, the authors propose a residual network model based on the channel and spatial attention mechanisms to reconstruct the self-luminous image of a flame from schlieren images of the flow field of a scramjet. The reconstruction of the self-luminous image of a flame through deep learning can inform research on the characteristics of combustion of a scramjet.
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