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Wavefront estimation through structured detection in laser scanning microscopy

Biomedical Optics Express 2025年第5期16卷 2135-2155页

作者： Fersini, Francesco Zunino, Alessandro Morerio, Pietro Baldini, Francesca Diaspro, Alberto Booth, Martin J. Del Bue, Alessio Vicidomini, Giuseppe Molecular Microscopy and Spectroscopy Istituto Italiano di Tecnologia Genoa Italy Dipartimento di Informatica Bioingegneria Robotica e Ingegneria dei Sistemi University of Genoa Genoa Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genoa Italy Nanoscopy and NIC@IIT Istituto Italiano di Tecnologia Genoa Italy Department of Physics University of Genoa Genoa Italy Department of Engineering Science University of Oxford Oxford United Kingdom

Laser scanning microscopy (LSM) is the base of numerous advanced imaging techniques, including confocal laser scanning microscopy (CLSM), a widely used tool in life sciences research. However, its effective resolution is often compromised by optical aberrations, a common challenge in all optical systems. While adaptive optics (AO) can correct these aberrations, current methods face significant limitations: aberration estimation, which is central to any AO approach, typically requires specialized hardware or prolonged sample exposure, rendering these methods sample-invasive, and less user-friendly. In this study, we propose a simple and efficient AO strategy for CLSM systems equipped with a detector array – image-scanning microscopy – and an AO element for beam shaping. We demonstrate, for the first time, that datasets acquired with a detector array inherently encode aberration information. As a proof-of-concept of this important property, we designed a custom convolutional neural network capable of decoding aberrations up to the 11th Zernike coefficient, directly from a single acquisition. While this data-driven approach represents an initial exploration of the aberration content, it opens the door to more advanced decoding strategies – including model-based methods. This work establishes a new paradigm for aberration sensing in LSM and is designed to work synergistically with conventional AO approaches such as phase diversity, enabling faster, less invasive, and more accessible high-resolution imaging. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Aberrations

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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying

arXiv

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arXiv 2025年

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang The School of Software Engineering Xi’an Jiaotong University Xi’an China The Tencent AI Lab Shenzhen China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The Center for Machine Vision and Signal Analysis University of Oulu Finland

关键词： Heart

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"Connected to the people": Social Inclusion & Cohesion in Action through a Cultural Heritage Digital Tool

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Proceedings of the ACM on Human-computer Interaction 2023年第CSCW2期7卷 1-37页

作者： Nisi, Valentina Bala, Paulo Cesário, Vanessa James, Stuart Del Bue, Alessio Nunes, Nuno Jardim Instituto Superior Técnico U. Lisbon Lisbon Portugal Visual Geometry and Modelling Lab Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy

Current cultural policies are evolving from social inclusion (removing barriers and promoting equality for participation in culture) to social cohesion (fostering solid bonds between groups despite their differences). Digital interventions can create spaces that promote social inclusion and cohesion. In this paper, we report on the design and evaluation of a cultural heritage and digital storytelling application supporting a participatory approach to culture and hosting society. We evaluate our intervention in three marginalized communities with different social-cultural contexts: migrant women in Barcelona, a community living in a priority neighbourhood in Paris and second and third-generation migrants in Lisbon. Through an analysis of their application use, our findings point at their needs and desires, highlighting how the app can support social inclusion as the first step towards cohesion, but that these are heterogeneous concepts susceptible to nuanced appropriations by the different communities. © 2023 ACM.

关键词： Digital devices

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Multi robot SLAM for features based environment modelling

Multi robot SLAM for features based environment modelling

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11th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014

作者： Riaz Un Nabi Jafri, Syed Ahmed, Waheed Ashraf, Zubair Chellali, Ryad Electronic Engineering Department NED UET Karachi Pakistan Pattern Analysis and Computer Vision LAB-IIT Genova Italy

ISBN: (纸本)9781479939787

This paper is presenting a multi robot simultaneous localization and mapping (SLAM) framework for environment modelling using 2D and 3D features. The proposed solution is using a team of mobile robots which are exploring unknown environment with unknown poses. Each team member is allowed to build its independent features based SLAM solution and to share the local map model among other mates. By matching the overlapping tendency of any two mates, a map merging strategy is introduced which in result is building global map. The overall approach has tested in 2D and 3D features based environment and results have shown. © 2014 IEEE.

关键词： Extended Kalman filters

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DarkGAN: Night Image Enhancement Using Generative Adversarial Networks 5th

DarkGAN: Night Image Enhancement Using Generative Adversaria...

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5th International Conference on computer vision and Image Processing, CVIP 2020

作者： Alaspure, Prasen Hambarde, Praful Dudhane, Akshay Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab IIT Ropar Rupnagar India

ISBN: (纸本)9789811610851

Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data or on the images which are considerably visible. In this paper, we propose a method to enhance real-world night time images, which are dark and noisy. The proposed DarkGAN consists of two pairs of Generator - Discriminator. Moreover, the proposed network enhances dark shades and removes noise up to a much extent, with natural-looking colors in the output image. Experimental results evaluation of the proposed method on the "See In the Dark" dataset demonstrates the effectiveness of the proposed model compared with other state-of-the-art methods. The proposed method yields comparable better results on qualitative and quantitative assessments when compared with the existing methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Image analysis and Processing — ICIAP 2015 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Murino Enrico Puppo

ISBN: (数字)9783319232348

ISBN: (纸本)9783319232331

The two-volume set LNCS 9279 and 9280 constitutes the refereed proceedings of the 18th International Conference on Image analysis and Processing, ICIAP 2015, held in Genoa, Italy, in September 2015. The 129 papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in the following seven topical sections: video analysis and understanding, multiview geometry and 3D computer vision, pattern recognition and machine learning, image analysis, detection and recognition, shape analysis and modeling, multimedia, and biomedical applications.

关键词： Image Processing and computer vision

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Image analysis and Processing — ICIAP 2015 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Vittorio Murino Enrico Puppo

ISBN: (数字)9783319232317

ISBN: (纸本)9783319232300

关键词： Image Processing and computer vision pattern Recognition Artificial Intelligence Algorithm analysis and Problem Complexity computer Graphics

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Depth Estimation From Single Image And Semantic Prior

Depth Estimation From Single Image And Semantic Prior

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IEEE International Conference on Image Processing

作者： Praful Hambarde Akshay Dudhane Prashant W. Patil Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab IIT Ropar

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The multi-modality sensor fusion technique is an active research area in scene understating. In this work, we explore the RGB image and semantic-map fusion methods for depth estimation. The LiDARs, Kinect, and TOF depth sensors are unable to predict the depth-map at illuminate and monotonous pattern surface. In this paper, we propose a semantic-to-depth generative adversarial network (S2D-GAN) for depth estimation from RGB image and its semantic-map. In the first stage, the proposed S2D-GAN estimates the coarse level depthmap using a semantic-to-coarse-depth generative adversarial network (S2CD-GAN) while the second stage estimates the fine-level depth-map using a cascaded multi-scale spatial pooling network. The experimental analysis of the proposed S2D-GAN performed on NYU-Depth-V2 dataset shows that the proposed S2D-GAN gives outstanding result over existing single image depth estimation and RGB with sparse samples methods. The proposed S2D-GAN also gives efficient results on the real-world indoor and outdoor image depth estimation.

关键词： Estimation Semantics Generators Robot sensing systems Laser radar Generative adversarial networks Training

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DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly

DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D ...

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Conference on computer vision and pattern Recognition (CVPR)

作者： Gianluca Scarpellini Stefano Fiorini Francesco Giuliari Pietro Morerio Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore, we highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving. Code available at https://***/iit-PAVIS/DiffAssemble.

关键词： Training Solid modeling Three-dimensional displays Noise reduction Noise Diffusion models Graph neural networks

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