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This paper proposes an imaging technique to remove strong reflections from specular surfaces using polarization characteristics combined with light field imaging. Firstly, the correct strong reflection region is found by studying the reflected light characteristics, and the strong reflection region highlights are filtered out using Stokes parameters based on polarization information. Then, a system of microlens arrays with different transmittances was built for imaging, and the system was image-corrected to enable more information about the scene to be captured.
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Electrical and Computer Engineering, University of Massachusetts Lowell, Ball Hall, 1 University Ave, Lowell, Massachusetts, 01854, UNITED STATES.
Objective: X-ray photon-counting detectors (PCDs) have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality.
View Article and Find Full Text PDFNon-Ablative Fractional 1940-nm Diode Laser for Skin Resurfacing and Treatment of Benign Pigmented Lesions.
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Background: The non-ablative 1940-nm laser induces controlled thermal damage at superficial depths without ablating the epidermis.
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Swin-transformer for weak feature matching.
Sci Rep
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Department of Computer Science and Technology, Qilu University of Technology, No. 3501 Daxue Road, Jinan, 250300, Shandong, China.
Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures.
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