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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

共 14747 条记录，以下是191-200 订阅

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High-Fidelity 4× Neural reconstruction of Real-time Path Traced images

High-Fidelity 4× Neural Reconstruction of Real-time Path Tr...

引用

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Zhiqiang Lao Yu Guo Xiyun Song Yubin Zhou Zongfang Lin Heather Yu Liang Peng Futurewei Technology US

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The growing demand for high-resolution, realistic rendering has significantly increased the workload for real-time path tracing, putting considerable strain on most graphics cards. One common approach to reduce the burden is rendering images at lower resolutions with fewer samples per pixel (spp) and then applying reconstruction techniques, such as denoising and upsampling, to achieve high-quality rendering at the desired resolution. Recovering fine details directly from noisy, low-resolution (LR) inputs in high-resolution (HR) images is challenging. There-fore, a two-stage reconstruction process is typically employed, treating denoising and upsampling as separate steps. We propose an integrated deep learning model based on a Generative Adversarial Network (GAN) to produce high-quality reconstructed images - i.e., denoised and up-sampled - for efficient, adaptive image reconstruction. We employ a self-adaptive data augmentation strategy to iteratively reduce residual noise and recover high-frequency details, continuously improving the output. Our method significantly reduces rendering overhead while minimizing quality loss. Experimental results show consistent performance in demanding 4 x upscaling scenarios, delivering real-time performance with enhanced quality and substantial improvements.

关键词： Computer vision Noise reduction Superresolution Noise Rendering (computer graphics) Generative adversarial networks Real-time systems Noise measurement image reconstruction Strain

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ROSA: Reconstructing Object Shape and Appearance Textures by Adaptive Detail Transfer

ROSA: Reconstructing Object Shape and Appearance Textures by...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Julian Kaltheuner Patrick Stotko Reinhard Klein University of Bonn

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Reconstructing an object's shape and appearance in terms of a mesh textured by a spatially-varying bidirectional reflectance distribution function (SVBRDF) from a limited set of images captured under collocated light is an ill-posed problem. Previous state-of-the-art approaches either aim to reconstruct the appearance directly on the geometry or additionally use texture normals as part of the appearance features. However, this requires detailed but inefficiently large meshes, that would have to be simplified in a post-processing step, or suffers from well-known limitations of normal maps such as missing shadows or incorrect silhouettes. Another limiting factor is the fixed and typically low resolution of the texture estimation resulting in loss of important surface details. To overcome these problems, we present ROSA, an inverse rendering method that directly optimizes mesh geometry with spatially adaptive mesh resolution solely based on the image data. In particular, we refine the mesh and locally condition the surface smoothness based on the estimated normal texture and mesh curvature. In addition, we enable the reconstruction of fine appearance details in high-resolution textures through a pioneering tile-based method that operates on a single pre-trained decoder network but is not limited by the network output resolution.

关键词： Geometry Adaptation models Surface reconstruction Shape Stability criteria Rendering (computer graphics) Decoding Surface texture Spatial resolution image reconstruction

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GANESH: Generalizable NeRF for Lensless Imaging

GANESH: Generalizable NeRF for Lensless Imaging

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Rakesh Raj Madavan Akshat Kaimal Badhrinarayanan K V Vinayak Gupta Rohit Choudhary Chandrakala Shanmuganathan Kaushik Mitra Shiv Nadar University Chennai Indian Institute of Technology Madras

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Lensless imaging offers a significant opportunity to develop ultra-compact cameras by removing the conventional bulky lens system. However, without a focusing element, the sensor's output is no longer a direct image but a complex multiplexed scene representation. Traditional methods have attempted to address this challenge by employing learnable inversions and refinement models, but these methods are primarily designed for 2D reconstruction and do not generalize well to 3D reconstruction. We introduce GANESH, a novel framework designed to enable simultaneous refinement and novel view synthesis from multi-view lensless images. Unlike existing methods that require scene-specific training, our approach supports on-the-fiy inference without retraining on each scene. Moreover, our framework allows us to tune our model to specific scenes, enhancing the rendering and refinement quality. To facilitate research in this area, we also present the first multi-view lensless dataset, LenslessScenes. Extensive experiments demonstrate that our method outperforms current approaches in reconstruction accuracy and refinement quality. Code and video results are available here.

关键词： Training Solid modeling Three-dimensional displays Virtual reality Rendering (computer graphics) Neural radiance field Robustness Wearable devices image reconstruction Synthetic data

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On-the-fly image Compression Using Overfitted Shallow CNN Autoencoders

On-the-fly Image Compression Using Overfitted Shallow CNN Au...

引用

International image Processing, Applications and Systems conference (IPAS)

作者： John El Gallab Abdelwahab Ganna Abdallah Kassem Hend Makram Martina Muawad Mina Mikhael Tamer ElBatt Computer Science and Engineering The American University in Cairo Cairo Egypt

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional Neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned image Compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times.

关键词： Measurement image quality image coding Autoencoders Transform coding Bandwidth Internet Convolutional neural networks Time factors image reconstruction

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image reconstruction from incomplete data VI

Image Reconstruction from Incomplete Data VI

引用

image reconstruction from incomplete data VI

ISBN: (纸本)9780819482969

The proceedings contain 24 papers. The topics discussed include: a layered media approach to photoacoustic tomography;investigation of limited-view image reconstruction in optoacoustic tomography employing a priori structural information;sampling rates and image reconstruction from scattered fields;imposing spatio-temporal support in magnetic resonance angiographic imaging;weighted least-squares image reconstruction in phase-contrast tomography;three-dimensional image visualization by maximum a posteriori estimation photon-counting integral imaging;in situ determination of wind fields from sailplane flight data;information theoretic characterizations of compressive-sensing-based space object identification;compressive phase contrast tomography;graphics processing unit restoration of non-uniformly warped images using a typical frame as prototype;and multiple image reconstruction for high-resolution optical imaging using structured illumination.

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image reconstruction from incomplete Radon data and Generalized Principal Component Analysis

Image Reconstruction from Incomplete Radon Data and Generali...

引用

作者： SL-GHI CHOI The University of Texas Arlington

学位级别：博士

image reconstruction in various types of tomography requires inversion of the Radon transform and its generalizations. While there are many stable and robust algorithms for such inversions from reasonably well sampled data, most of these algo- rithms fail when applied to limited view data. In the dissertation we develop a new method of stable reconstruction from limited view data for functions, whose support is a union of finitely many circles. Such images, among other things, are good approx- imations of tomograms of certain types of tumors in lungs. Our method is based on a modified version of GPCA (Generalized Principal Component Analysis) and some results from algebraic geometry.

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Dual-energy CT metal artifact reduction by combined material decomposition and projection domain threshold segmentation

Dual-energy CT metal artifact reduction by combined material...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kai Chen Tianling Lyu Jean-Louis Coatrieux Yang Chen Nanjing University Of Posts And Telecommunications Southeast University Nanjing China Zhejiang Lab Hangzhou China Laboratoire Traitement du Signal et de l’Image Rennes France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-energy CT exploits the different attenuation characteristics of substances under different energy X-rays and collects high- and low-energy data from the same area to differentiate and quantify specific substances, which is now widely used in clinical diagnosis, disease monitoring, and other fields. If the object scanned during dual-energy CT imaging contains metallic material, the reconstructed image will suffer from metal artifacts. Metal artifacts in dual-energy CT result in surrounding tissue structures presenting erroneous CT values, blurred image details, and unclear border demarcation lines, which seriously affects the quality of the images as well as the accuracy of clinical diagnosis. To reduce metal artifacts in dual-energy CT, we combine material decomposition techniques with metal artifact reduction to explore metal artifact reduction methods applied to dual-energy CT.

关键词： Computed tomography Metallic materials Metals Imaging X-rays Signal processing Clinical diagnosis Speech processing image reconstruction Monitoring

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Unsupervised Generative Approach for Anomaly Detection to Enhance the Quality of Unseen Medical datasets

Unsupervised Generative Approach for Anomaly Detection to En...

引用

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Zhemin Zhang Bhavika Patel Bhavik Patel Imon Banerjee Arizona State University Tempe AZ Mayo Clinic AZ Phoenix AZ

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Out-of-distribution (OOD) and anomaly detection are critical for reducing noise and improving the generalization of AI models in breast cancer screening, especially when handling unseen private data. Given the challenge of limited prior knowledge about OOD samples in external unseen datasets, unsupervised generative learning is a preferable solution which trains the model to discern the normal characteristics of in-distribution (ID) data. The hypothesis is that during inference, the model aims to reconstruct ID samples accurately, while OOD samples exhibit poorer reconstruction due to their divergence from normality. In-spired by state-of-the-art (SOTA) hybrid architectures combining CNNs and transformers, we developed a novel back-bone - HAND (Hybrid ANomaly Detection), for detecting OOD from digital mammogram studies. To boost learning efficiency, we incorporated synthetic OOD samples and a parallel discriminator in the latent space to distinguish between ID and OOD samples. Gradient reversal to the OOD reconstruction loss penalizes the model for learning OOD reconstructions. An anomaly score is computed by weighting the reconstruction and the discriminator loss. On internal held-out test and external dataset, the proposed HAND model outperformed encoder-based and GAN-based baselines, and interestingly, it also outperformed the hybrid CNN+transformer baselines. Proposed HAND pipeline of-fers an automated and efficient computational solution for domain-specific quality checks in external screening mam-mograms, yielding actionable insights without direct exposure to private medical imaging data.

关键词： Hands Computational modeling conferences Pipelines Noise Transformers data models Mammography image reconstruction Anomaly detection

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Spatial-Frequency Cross-Domain Mutual Guidance for Visible-Infrared image Fusion

Spatial-Frequency Cross-Domain Mutual Guidance for Visible-I...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhili Lin Biao Leng School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary objective of Visible-Infrared image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transformations, which results in fused images lacking sufficient detail and failing to effectively preserve crucial details from the source images. In this paper, we propose Spatial-Frequency Mutual Guidance for VIF. The framework comprises two branches. Each branch reconstructs input features in the frequency and spatial domains. We introduce a novel cross-domain mutual guidance mechanism. It fully integrates information between the frequency and spatial domains to enhance the detail quality of fused images. Furthermore, a weight allocation network is utilized to adaptively assign importance to visible and infrared images based on scene characteristics. Experiments on three VIF datasets show that our method outperforms recent advanced algorithms.

关键词： image color analysis Frequency-domain analysis Heuristic algorithms Signal processing algorithms Signal processing Resource management Speech processing image fusion image reconstruction Visual perception

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Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography

Indoor Airflow Imaging Using Physics-Informed Schlieren Tomo...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Arjun Teh Wael H. Ali Joshua Rapp Hassan Mansour Carnegie Mellon University Pittsburgh PA USA Mitsubishi Electric Research Laboratories Cambridge MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Remote temperature sensing of volumetric flows has a variety of applications, such as promoting thermal comfort, heat dissipation, or data center cooling. The emergence of background-oriented schlieren (BOS) imaging in recent years has enabled transparent flow visualization at minor costs. In this paper, we develop a framework for non-invasive volumetric indoor airflow estimation from a single viewpoint using BOS measurements and physics-informed reconstruction. Our framework utilizes a light projector that projects a pattern onto a target back wall and a camera that observes small distortions in the light pattern due to the change in the refractive index of the air as a result of the temperature variation. While the single-view BOS tomography problem is severely ill-posed, we regularize the reconstruction using a physics-informed neural network (PINN) that ensures that the reconstructed airflow is consistent with the coupled Boussinesq approximation of the incompressible Navier– Stokes and the heat transfer equations.

关键词： Temperature measurement Temperature sensors Temperature distribution Volume measurement Neural networks Refractive index Tomography Sensors Speech processing image reconstruction

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