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conference on Photonic Instrumentation Engineering X

作者： Jahn, Wilfried Mehri, Tahar SILINA 880 Route Mimet Gardanne France

ISBN: (纸本)9781510659612;9781510659629

Curved imaging sensors bring significant size, weight and cost reduction to imaging systems while mitigating off-axis optical aberrations, as opposed to current flat sensors. Unlocking these key features has captured the interest of major players over the last two decades. SILINA has been developing a CMOS image Sensor (CIS) curving process, which adapts to various sensor characteristics. This enables to maximize the optical performance of every single imaging system. We have demonstrated the manufacturing of curved CMOS Front-Side Illuminated (FSI) and Back Side Illuminated (BSI), opening a new area of compact, fast, wide-angle and high-resolution optical lenses. This new degree of freedom offered to optical designer can significantly simplify optical systems through a significant improvement of the optical performance while simplifying the system architecture in many different ways. The field of view (FOV), the contrast, the aperture can be increased while optical aberrations can be minimized. At the end, the different costs related to manufacturing, metrology, integration, and alignment are reduced. This is of great importance for applications requiring compact and high resolution lens, notably in low-light environment. To quantify the gain brought by curved image sensor for smartphone camera lens, we are performing several comparative optical lens designs. We compare traditional flat-image sensor based camera lens to camera lens optimized specifically with a curved image sensor. In this paper, we present the result obtained on wide-angle smartphone camera lens design considering a spherical concave image sensor. We compare the optical characteristics and performance with a reference optical design using a flat image sensor. We discuss the various benefits in terms of optical performance and Z-stack reduction.

关键词： Curved CMOS image Sensor Optical design Smartphone Camera lens

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Fλ/d as a predictor of image classification algorithm performance

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APPLIED OPTICS 2025年第4期64卷 845-854页

作者： Hixson, Jonathan g. Teaney, Brian Finch, Michael f. Nehmetallah, George Driggers, Ronald US Army Combat Capabil Dev Command DEVCOM C5ISR Ctr RTI Aberdeen Proving Ground NY 21005 USA Catholic Univ Amer EECS Dept 620 Michigan Ave NE Washington DC 20064 USA Univ Arizona Wyant Coll Opt Sci Infrared Syst Grp Tucson AZ 85721 USA

This research studied the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of machine learning algorithms such as the YOLO (You Only Look Once) algorithm for object classification. The YOLO_v3 and YOLO_v10 algorithms were trained using static imagery provided in the commonly available training dataset provided by Teledyne FLIR systems. image processing techniques were used to degrade image quality of the test dataset also provided by Teledyne FLIR systems, simulating detector-limited to optics-limited performance, which results in a variation of the FLD metric between 0.339 and 7.98. The degraded test set was used to evaluate the performance of YOLO_v3 and YOLO_v10 for object classification and relate the FLD metric to the probability of detection. Results of YOLO_v3 and YOLO_v10 are presented for the varying levels of image degradation. A summary of the results is discussed along with recommendations for evaluating an algorithm's performance using a sensor's FLD metric value. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： image quality image sensors imaging systems imaging techniques Machine learning Sensor performance

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Artificial compound eye camera for wide field of view and high-resolution imaging 6

Artificial compound eye camera for wide field of view and hi...

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International conference on Flexible and Printable sensors and systems (FLEPS)

作者： Chang, Sehui Song, Young Min Gwangju Inst Sci & Technol GIST Sch Elect Engn & Comp Sci Gwangju South Korea GIST AI Grad Sch Gwangju South Korea GIST Dept Semicond Engn Gwangju South Korea

ISBN: (纸本)9798350383270;9798350383263

Insect compound eyes have been garnered a great attention in the fields of optical design, owing to their remarkably economical, but sophisticated visual structures and process. However, a rigid and planar imaging sensor hardly supporting curved micro-optics hinders full implementation of insect vision system. Here, we present a bioinspired artificial compound eye camera for wide field of view and high-resolution imaging based on the hemispherical microlens array and tapered imaging fiber bundles. The hemispherical microlens array focuses a wide field of view image on the curved facet of tapered fiber bundle, which relays the image to another facet contacted on commercial image sensor providing high resolution. The integrated compound eye module suggests a facile way to fill the technical gap for practical applications of artificial insect vision systems.

关键词： bioinspired vision compound eye micro-optics optoelectronics

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Motion Estimation for Quanta image sensors Using Spatio-Temporal Priors

Motion Estimation for Quanta Image Sensors Using Spatio-Temp...

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2024 conference on Visual Communications and image Processing

作者： Fukawa, Hiroya Kurihara, Kosuke Maeda, Yoshihiro Sato, Shunichi Hamamoto, Takayuki Tokyo Univ Sci Tokyo 1258585 Japan Shibaura Inst Technol Tokyo 1358548 Japan

ISBN: (纸本)9798331529543;9798331529550

Quanta image sensors are a novel paradigm in image sensor technology. Their direct application to quanta image sensors-based imaging systems is challenging because a bit-plane image is a set of binary images. In this paper, we introduce spatiotemporal priors based on the intensity invariance and smoothness characteristics of the motion vector. Specifically, we model when the image sequences align with the correct motion vector, the spatiotemporal structure becomes more consistent. Moreover, the spatial smoothness prior is incorporated through the smoothing filtering of the evaluation metrics of motion vector candidates. The experimental results show that the proposed method is more effective than conventional methods.

关键词： motion estimation quanta image sensors bit-plane images spatio-temporal prior

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Large-Scale Dense 3-D Mapping Using Submaps Derived From Orthogonal imaging Sonars

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第1期50卷 354-369页

作者： McConnell, John Collado-Gonzalez, Ivana Szenher, Paul Englot, Brendan US Naval Acad Dept Weap Robot & Control Engn Annapolis MD 21402 USA Stevens Inst Technol Dept Mech Engn Hoboken NJ 07030 USA

3-D situational awareness is critical for any autonomous system. However, when operating underwater, environmental conditions often dictate the use of acoustic sensors. These acoustic sensors are plagued by high noise and a lack of 3-D information in sonar imagery, motivating the use of an orthogonal pair of imaging sonars to recover 3-D perceptual data. Thus far, mapping systems in this area only use a subset of the available data at discrete timesteps and rely on object-level prior information in the environment to develop high-coverage 3-D maps. Moreover, simple repeating objects must be present to build high-coverage maps. In this work, we propose a submap-based mapping system integrated with a simultaneous localization and mapping system to produce dense, 3-D maps of complex unknown environments with varying densities of simple repeating objects. We compare this submapping approach to our previous works in this area, analyzing simple and highly complex environments, such as submerged aircraft. We analyze the tradeoffs between a submapping-based approach and our previous work leveraging simple repeating objects. We show where each method is well-motivated and where they fall short. Importantly, our proposed use of submapping achieves an advance in underwater situational awareness with wide aperture multibeam imaging sonar, moving toward generalized large-scale dense 3-D mapping capability for fully unknown complex environments.

关键词： Sonar imaging Simultaneous localization and mapping Robot sensing systems Apertures Feature extraction Robot kinematics image segmentation Underwater vehicles Three-dimensional displays Autonomous underwater vehicles (AUVs) simultaneous localization and mapping (SLAM) sonar imaging and ranging

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Target Association of Heterogeneous sensors Based on Attribute-Position Fusion 8th

Target Association of Heterogeneous Sensors Based on Attribu...

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8th International conference on Information Science, Communication and Computing

作者： Xia, Qiuyin Shi, Limin Zou, Jiancheng Weng, Lubin North China Univ Technol Coll Sci Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat Aerosp Informat Res Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

Associating targets detected by heterogeneous imaging sensors is a key issue in Target recognition based on multi-sensor image fusion. Traditional algorithms often use a single type of information from the image to calculate the cost matrix between sensor-detected targets, such as attribute information or position information. When multiple targets have similar information, these methods often lead to incorrect associations and are susceptible to sensor system errors and environmental factors. However, as technology advances, the information that sensors can detect is becoming more diverse and abundant. Thus, this paper proposes an algorithm that combines grey relational analysis based on target attribute information and DBSCAN clustering analysis based on position information. Using Dempster-Shafer evidence theory, the algorithm fuses the two types of information to achieve target association for heterogeneous imaging sensors. According to Monte Carlo simulation experiments using ships as targets, comparative experiments were conducted with grey relational analysis based on attribute information, bias mapping clustering based on position information, and the weighted bipartite graph optimal solution algorithm that uses both attribute and position information as features. The experimental results indicate that the algorithm proposed in this paper can overcome the limitations of single-information target association. It effectively mitigates the impacts of false alarms and positional distribution, thereby improving the accuracy of target association for heterogeneous imaging sensors.

关键词： Heterogeneous imaging sensors Target Association Attribute Information Position Information Grey Relational Analysis DBSCAN Dempster-Shafer Evidence Theory

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A Live Demo of Single-Photon imaging and Applications

A Live Demo of Single-Photon Imaging and Applications

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SIGGRAPH conference on Emerging Technologies

作者： Jungerman, Sacha Sundar, Varun Gupta, Mohit Univ Wisconsin Madison Madison WI 53706 USA

ISBN: (纸本)9798400705243

Single-photon sensors are a novel class of imaging sensors that are sensitive to the individual arrival of photons. In recent years, single-photon sensors have witnessed rapid growth in key sensor characteristics such as array resolution, which, for the first time, has made computer vision and imaging with these emerging sensors possible. This rapid growth has also spurred the development of algorithms that can harness the rich spatio-temporal scene information captured by single-photon sensors, which has led to several new imaging capabilities. These capabilities include imaging in challenging SNR conditions, high-dynamic range imaging, compensating extreme camera motion extents, and even providing the functionality of numerous imaging systems via post-capture processing. Our demo will showcase these exciting capabilities to a wide computer vision and graphics audience, and in doing so, make a case for the mainstream adoption of single-photon technology.

关键词： Single-photon camera single-photon avalanche diode quanta image sensors high dynamic range high-speed imaging low-light imaging motion compensation software-defined cameras.

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Samsung Res China Nanjing Peoples R China Nankai Univ Tianjin Peoples R China

ISBN: (纸本)9798350365474

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： computer vision flare removal image restoration

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Increasing the Diversity in RGB-to-Thermal image Translation for Automotive Applications

Increasing the Diversity in RGB-to-Thermal Image Translation...

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2024 IEEE sensors conference

作者： Wang, Kaili Ravaglia, Leonardo Longo, Roberto Goetschalckx, Lore Van Hammel, David Moeyersoms, Julie Ben Stoffelen De Schepper, Tom IMEC B-3001 Leuven Belgium Imec IPI Ghent Univ B-9000 Ghent Belgium

ISBN: (纸本)9798350363524;9798350363517

Thermal imaging in Advanced Driver Assistance systems (ADAS) improves road safety with superior perception in low-light and harsh weather conditions compared to traditional RGB cameras. However, research in this area faces challenges due to limited dataset availability and poor representation in driving simulators. RGB-to-thermal image translation offers a potential solution, but existing methods focus on one-to-one mappings. We propose a one-to-many mapping using a multi-modal translation framework enhanced with our Component-aware Adaptive Instance Normalization (CoAdaIN). Unlike the original AdaIN, which applies styles globally, CoAdaIN adapts styles to different image components individually. The result, as we show, is more realistic and diverse thermal image translations.

关键词： image translation Style transfer RGB-to-thermal translation GAN model Automotive driving

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Optimization of color-coded apertures for compressive multispectral and depth imaging with time-of-flight sensors 13

Optimization of color-coded apertures for compressive multis...

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conference on Dimensional Optical Metrology and Inspection for Practical Applications XIII

作者： Rueda-Chacon, Hoover Arce, Gonzalo R. Univ Ind Santander Carrera 27 Calle 9 Bucaramanga Colombia Univ Delaware 140 Evans Hall Newark DE USA

ISBN: (纸本)9781510673953;9781510673946

The demand for new image sensors that can simultaneously acquire multispectral-and-depth imagery (MS-D) using compact, lightweight and monocular imaging systems is rapidly increasing. Seminal works in this line focused on RGB-D sensors that were able to acquire 3-channel color images and a depth map, but relying on two independent image sensors. We intend to advance the state-of-the-art in imaging systems with the ability to extract depth while accurately capturing spectral properties of scenes using as few as a single snapshot. Therefore, this paper discusses the advances of a compressive spectral-depth computational camera that employs a time-of-flight (ToF) sensor, an optimized color-coded aperture (CCA), a dispersive element and a model-based reconstruction algorithm, to attain MS-D imaging. In particular, the ToF sensor has the ability to measure ambient light along with modulated light from the active source, and the CCA is a passive-and-static optical element optimized to spectrally encode the scene reflectance while permitting the propagation of the active modulated light unaffected. The optimization of the CCA is performed using a direct-binary-search (DBS) algorithm that exploits the underlying ideas of blue-noise multitoning, to design the spatial distribution and spectral response characteristics of each one of the optical filters (pixels) of the CCA. We report a proof-of-concept prototype of such a camera that uses a CCA fabricated with 3 different filters using deposition and lithographic patterning cycles of thin-films.

关键词： Color-coded aperture compressive sensing spectral imaging depth imaging time of flight

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