We study a way to take into account the scene illumination spectrum during end-to-end optimization of optical-digital hybrid systems that include annular binary phase masks to enhance their depth of field (DoF). We show that a phase mask specifically optimized for wide spectrum panchromatic imaging performs better under this type of illumination than phase masks optimized under monochromatic illumination assumption. Indeed, thanks to spectral averaging, the modulation transfer functions of such a phase mask are very close to each other. This guarantees a very homogeneous image quality across the DoF range, which we demonstrate theoretically and experimentally using a dedicated optical setup.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/AO.457214 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
An End-To-End Speech Recognition Model for the North Shaanxi Dialect: Design and Evaluation.
Sensors (Basel)
January 2025
SHCCIG Yubei Coal Industry Co., Ltd., Xi'an 710900, China.
The coal mining industry in Northern Shaanxi is robust, with a prevalent use of the local dialect, known as "Shapu", characterized by a distinct Northern Shaanxi accent. This study addresses the practical need for speech recognition in this dialect. We propose an end-to-end speech recognition model for the North Shaanxi dialect, leveraging the Conformer architecture.
View Article and Find Full Text PDFSeed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.
Sensors (Basel)
January 2025
Department of Earth, Environment and Geospatial Sciences, Saint Louis University, Saint Louis, MO 63108, USA.
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands.
View Article and Find Full Text PDFCost and outcomes of side-to-side versus end-to-end anastomosis in right colectomy: a retrospective cohort study.
Surg Endosc
January 2025
Clarunis University Digestive Health Care Center Basel, Basel, Switzerland.
Background: The optimal anastomotic configuration for right-sided colectomies remains controversial, with comparable postoperative outcomes across techniques. Thus, economic considerations may play a larger role in decision-making within cost-constrained healthcare settings.
Methods: This retrospective cohort study evaluated right-sided colectomies with ileocolic anastomosis at a Swiss tertiary center between 2016 and 2021.
EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module.
Biomimetics (Basel)
January 2025
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories.
View Article and Find Full Text PDFDeep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study.
Bioengineering (Basel)
January 2025
Department of Intelligent Robotics, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!