Fringe projection profilometry based on MEMS micro-vibration mirrors is very promising due to its rapid projection, large depth of field, compact size, and low cost. Although high-frequency fringes can achieve accurate reconstruction, the projector must offer sufficient pixel resolution. In this paper, we proposed a high-resolution projection technique called the delay superposition method. During a single exposure time of the camera, the projector projects a group of low-resolution fringe patterns, which are delayed according to the movement characteristics of the vibration mirror. Then, the camera exposure superimposes these low-resolution images to form a high-resolution image. These two steps effectively subdivide the angle intervals, thereby achieving a pixel interpolation. Finally, experimental results show that the proposed method can significantly improve the projector's pixel resolution and reconstruction accuracy. The proposed method allows the MEMS projector's pixel resolution (along one direction) to far exceed that of common DLP projectors. It holds great application potential for high-frequency fringe projection.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OL.531846 | DOI Listing |
Biosensors (Basel)
January 2025
State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Fluorescence lifetime imaging (FLIM) has established itself as a pivotal tool for investigating biological processes within living cells. However, the extensive imaging duration necessary to accumulate sufficient photons for accurate fluorescence lifetime calculations poses a significant obstacle to achieving high-resolution monitoring of cellular dynamics. In this study, we introduce an image reconstruction method based on the edge-preserving interpolation method (EPIM), which transforms rapidly acquired low-resolution FLIM data into high-pixel images, thereby eliminating the need for extended acquisition times.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States.
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Department of Informatics, University of Oslo, 0316 Oslo, Norway.
In adaptive beamforming, the array signal processing adjusts its sensor delays and weights based on the incoming data. In conventional beamforming, these parameters are instead given from a predefined model. Adaptive beamformers can improve measurement precision by dynamically rejecting spatial interference.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Beijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green Applications Experimental Center of Advanced Materials, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Metal halide perovskites (MHPs) are promising materials for radiation detection. Compared with polycrystalline films, single crystals (SCs) have lower defect density, higher carrier mobility, and lifetime. However, the direct synthesis of MHP SCs for large-area flat panel imaging detectors remains challenging.
View Article and Find Full Text PDFData Brief
February 2025
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled "BDMANGO" has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!