Balancing speed and accuracy has always been a challenge in 3D reconstruction. One-shot structured light illuminations are of perfect performance on real-time scanning, while the related 3D point clouds are typically of relatively poor quality, especially in regions with rapid height changes. To solve this problem, we propose a one-shot reconstruction scheme based on shearlet transform, which combines spatial and frequency domain information to enhance reconstruction accuracy. First, we apply the shearlet transform to the deformed fringe pattern to obtain the transform coefficients. Second, pixel-wise select the indices associated with the N largest coefficients in magnitude to obtain a new filter. Finally, we refocus globally to extract phase using these filters and generate a reliable quality map based on coefficient magnitudes to guide phase unwrapping. Simultaneously, we utilize the maximum coefficient value to generate a quality map for guiding the phase unwrapping process. Experimental results show that the proposed method is robust in discontinuous regions, resulting in more accurate 3D point clouds.
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http://dx.doi.org/10.1364/OE.529603 | DOI Listing |
Comput Biol Med
February 2025
College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi Arabia. Electronic address:
Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve symptoms, a definitive cure remains elusive.
View Article and Find Full Text PDFPeerJ Comput Sci
November 2024
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia.
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis.
View Article and Find Full Text PDFMicrosc Res Tech
February 2025
Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India.
In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness.
View Article and Find Full Text PDFJ Comput Biol
August 2024
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN) architecture for glioma tumor detection. Our proposed methodology comprises a transformation module, a feature extraction module, and a tumor segmentation module.
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