Abstract. Visual inspection for a highly reflective surface is commonly faced with a serious limitation, which is that useful information on geometric construction and textural defects is covered by a parasitic image due to specular highlights. In order to solve the problem, we propose an effective method for removing the parasitic image. Specifically, a digital micromirror device (DMD) camera for programmable imaging is first described. The strength of the optical system is to process scene ray before image formation. Based on the DMD camera, an iterative algorithm of modulated region selection, precise region mapping, and multimodulation provides removal of the parasitic image and reconstruction of a correction image. Finally, experimental results show the performance of the proposed approach.
Computational photography has been a new research focus over the last two decades. Most of the computational imaging applications involve pixel-to-pixel correspondence adjustment between the spatial light modulator and the sensor. In this study, an accuracy pixel-to-pixel alignment method with six-axis adjustment is proposed. Specifically, the relations between the moiré fringe distribution and the six degree of freedom (DoF) displacements are characterized. A special pattern called five-grating pattern is designed for monitoring and adjusting the six DoF displacements in a four-step alignment procedure. Finally, the experimental results verify the performance of the proposed method by discussing the alignment accuracy.
The enhancement factor is one of the key parameters characterizing the phenomenon of surface-enhanced Raman scattering. At present, this parameter is described by an empirical formula or a certain single physical mechanism instead of a unified model of the chemical and electromagnetic enhancement mechanisms. It is necessary to integrate the dual enhancement mechanisms of SERS to more accurately obtain the SERS enhancement factor with molecular selectivity. Therefore, we propose a quantitative model for the prediction of the enhancement factor that includes the two main contributions, metal plasmon resonance and electronic structure. Theoretical analysis and verification by experimental results prove that the new predictive enhancement factor (EF) model of electronic structural energy improves the enhancement factor by approximately 10 times and can be used to calculate the enhancement factors of different molecules on the same substrate material, which can provide molecular selectivity and more accurate EF predictions. This paper presents a theoretical model of the SERS enhancement factor that includes the adsorption of the adsorbed molecules and the surface of the substrate, combines the electromagnetic and chemical enhancement mechanisms for surface-enhanced Raman scattering, and provides a deep comprehension of the phenomenon of surface-enhanced Raman scattering.
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