We propose a flexible-ratio adaptive point-spread spherical wave synthesis method for fast computer-generated hologram calculation. The conventional adaptive point-spread algorithm uses a fixed ratio between the major and minor axes of the point source, whereas the proposed method uses flexible-ratio sources, i.e., ellipses, for more effective calculation. Numerical simulation was conducted to validate the proposed method. The results show that the proposed method has the potential to achieve faster calculation, compared to the calculation in conventional methods, without significant image degradation.
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http://dx.doi.org/10.1364/JOSAA.34.001080 | DOI Listing |
Optical-resolution photoacoustic microscopy enables cellular-level biological imaging in deep tissues. However, acquiring high-quality spatial images without knowing the point spread function (PSF) at multiple depths or physically improving system performance is challenging. We propose an adaptive multi-layer photoacoustic image fusion (AMPIF) approach based on blind deconvolution and registration.
View Article and Find Full Text PDFPeerJ Comput Sci
November 2024
Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost.
View Article and Find Full Text PDFNat Commun
November 2024
Department of Chemistry, Rice University, Houston, TX, USA.
Diffraction-limited imaging in epi-fluorescence microscopy remains a challenge when sample aberrations are present or when the region of interest rests deep within an inhomogeneous medium. Adaptive optics is an attractive solution, albeit with limited field of view and requiring relatively complicated systems. Alternatively, reconstruction algorithms have been developed over the years to correct for aberrations.
View Article and Find Full Text PDFFeature detection and description are crucial for image matching, as better performance at this stage leads to more accurate matching results, which is essential for subsequent vision-based tasks. However, images captured by different optical systems may suffer from various optical aberrations, especially in off-axis field and out-of-depth-of-field regions, making it challenging for models to extract consistent feature locations and descriptors. In this paper, we propose what we believe to be a novel method for training feature detection and description networks by incorporating optical system aberrations modeled by point spread function(PSF).
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