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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181-190 订阅

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Unsupervised Learning from incomplete Measurements for Inverse Problems 36

Unsupervised Learning From Incomplete Measurements for Inver...

引用

36th conference on Neural Information Processing Systems (NeurIPS)

作者： Tachella, Julian Chen, Dongdong Davies, Mike CNRS Lab Phys Lyon France ENSL Lyon France Univ Edinburgh Sch Engn Edinburgh Midlothian Scotland

ISBN: (纸本)9781713871088

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be overcome by using measurements from multiple operators. While this idea has been successfully applied in various applications, a precise characterization of the conditions for learning is still lacking. In this paper, we fill this gap by presenting necessary and sufficient conditions for learning the underlying signal model needed for reconstruction which indicate the interplay between the number of distinct measurement operators, the number of measurements per operator, the dimension of the model and the dimension of the signals. Furthermore, we propose a novel and conceptually simple unsupervised learning loss which only requires access to incomplete measurement data and achieves a performance on par with supervised learning when the sufficient condition is verified. We validate our theoretical bounds and demonstrate the advantages of the proposed unsupervised loss compared to previous methods via a series of experiments on various imaging inverse problems, such as accelerated magnetic resonance imaging, compressed sensing and image inpainting.

关键词： Unsupervised learning

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High-efficiency infrared 3D reconstruction with hybrid SFM-MVS for large scenes

High-efficiency infrared 3D reconstruction with hybrid SFM-M...

引用

2024 International conference on Optical and Photonic Engineering, icOPEN 2024

作者： Yang, Mengnan Liu, Lizhaoxuan Zhou, Liuyao Feng, Shijie Hu, Yan Laboratory Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Traditional multi-view three-dimensional (3D) reconstruction uses images from visible light that may be of poor quality under conditions such as reflections, light spots, or haze, causing difficulties for 3D reconstruction in large scenes. To solve this problem, an efficient hybrid structure in motion (SFM) and multi-view stereo (MVS) point cloud reconstruction and alignment fusion method based on infrared images is proposed in this paper. Since thermal infrared imaging is less affected by lighting conditions and smoke, we adopt infrared images as the reconstruction raw dataset to make up for the defects of visible light 3D reconstruction;the reconstruction method adopts the hybrid SFM-MVS algorithm reconstruction with subset partitioning strategy, which can complete the dense reconstruction more efficiently under a large amount of image data. The iterative closest point (ICP) algorithm is used to perform the fusion of multi-angle reconstruction for the alignment of the point cloud, which can make up for the lack of side information in the large scene model reconstructed from a single angle. The experiment finally obtained a fairly good infrared 3D scene model with complete side information. © 2025 SPIE.

关键词： Stereo image processing

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Semantics-guided hierarchical feature encoding generative adversarial network for natural image reconstruction from brain activities

Semantics-guided hierarchical feature encoding generative ad...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Meng, Lu Yang, Chuanhao Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781665488679

The use of deep learning methods to decode visual perception images from brain activity recorded by fMRI has received a lot of attention. However, limited fMRI data make the task of visual reconstruction challenging. Inspired by hierarchical encoding of the visual cortex and the theory of brain homology with convolutional neural networks (CNNs), we propose a novel neural decoding model called hierarchical semantic generative adversarial network (HS-GAN). Specifically, we use CNN-based image encoder to extract hierarchical and semantic features of visually stimulus images. Then a neural decoder is used to decode hierarchical and semantic features from fMRI. In order to take full advantage of the information from different visual cortexes, we construct a generator with self-attention modules and skip connections to fuse the image features of different layers. In model training, adversarial learning is introduced to realize more natural image reconstruction. Compared to existing advanced methods, our method significantly improves the naturalness and fidelity of reconstructed images.

关键词： brain decoding fMRI GAN image reconstruction deep learning

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A Dual Cross Attention Transformer Network for Infrared and Visible image Fusion 7

A Dual Cross Attention Transformer Network for Infrared and ...

引用

7th International conference on Artificial Intelligence and Big data (ICAIBD)

作者： Zhou, Zhuozhi Lan, Jinhui Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350385113;9798350385106

Infrared and visible image fusion task aims to decompose and combine complementary information from both sensors. To overcome the lack of global intensity balance in fusion images, we proposed our joint transformer network with feature enhancement and stack cross attention (SCA) layer. Firstly, axis-based self-attention layers are applied to extract shallow features. Then, feature enhancement layer extracts feature from spatial and channel perspectives into stacks. Subsequently, the SCA layer employs cross attention for feature interaction between modalities and cross-layer attention for reassembling feature stacks to targeted pattern, which adaptively generates cross modality and feature layer relationships, respectively. Moreover, to tackle the issue of maintaining fusion by results-oriented metrics, we conduct decomposition loss to constrain above procedure by controlling cross modality correlation. Therefore, modality-specific and modality-general features are divided properly, facilitating feature reconstruction in the decoder. Finally, qualitative results show that our method preserves abundant texture and precise intensity from source images. Quantitative experimental results demonstrate that our fusion network achieves the state-of-the-art fusion performance, especially in mutual information (MI).

关键词： image fusion Attention mechanism Transformer Sensors Feature extraction

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Diffusion Posterior Sampling for Nonlinear CT reconstruction

Diffusion Posterior Sampling for Nonlinear CT Reconstruction

引用

conference on Medical Imaging - Physics of Medical Imaging

作者： Li, Shudong Tivnan, Matthew Stayman, J. Webster Johns Hopkins Univ Biomed Engn Baltimore MD 21218 USA Harvard Med Sch Radiol Boston MA USA Massachusetts Gen Hosp Boston MA USA

ISBN: (纸本)9781510671553;9781510671546

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior;which can then be incorporated with an arbitrary data model. However, current methods only rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

关键词： Diffusion Double positive medium Education and training CT reconstruction data modeling Reverse modeling Computed tomography Statistical modeling Stochastic processes Systems modeling

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EDICT: Exact Diffusion Inversion via Coupled Transformations

EDICT: Exact Diffusion Inversion via Coupled Transformations

引用

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

作者： Wallace, Bram Gokul, Akash Naik, Nikhil Salesforce Res San Francisco CA 94105 USA

ISBN: (纸本)9798350301298

Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The standard approach for real image editing with inversion uses denoising diffusion implicit models (DDIMs [29]) to deterministically noise the image to the intermediate state along the path that the denoising would follow given the original conditioning. However, DDIM inversion for real images is unstable as it relies on local linearization assumptions, which result in the propagation of errors, leading to incorrect image reconstruction and loss of content. To alleviate these problems, we propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion. Using Stable Diffusion [25], a state-of-the-art latent diffusion model, we demonstrate that EDICT successfully reconstructs real images with high fidelity. On complex image datasets like MS-COCO, EDICT reconstruction significantly outperforms DDIM, improving the mean square error of reconstruction by a factor of two. Using noise vectors inverted from real images, EDICT enables a wide range of image edits-from local and global semantic edits to image stylization-while maintaining fidelity to the original image structure. EDICT requires no model training/finetuning, prompt tuning, or extra data and can be combined with any pretrained DDM.

关键词： image and video synthesis and generation

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Prior Metadata-Driven RAW reconstruction: Eliminating the Need for Per-image Metadata 24

Prior Metadata-Driven RAW Reconstruction: Eliminating the Ne...

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Han, Wencheng Zhang, Chen Zhou, Yang Liu, Wentao Qian, Chen Xu, Cheng-Zhong Shen, Jianbing SKL-IOTSC CIS University of Macau China SenseTime Research and Tetras.AI Beijing China

ISBN: (纸本)9798400706868

While RAW images are efficient for image editing and perception tasks, their large size can strain camera storage and bandwidth. reconstruction methods of RAW images from sRGB data typically require additional metadata from the RAW image, which increases camera processing computations. To address this problem, we propose using Prior Meta as a reference to reconstruct the RAW data instead of relying on per-image metadata. Prior metadata is extracted offline from reference RAW images, which are usually part of the training dataset and have similar scenes and light conditions as the target image. With this prior metadata, the camera does not need to provide any extra processing other than the sRGB images, and our model can autonomously find the desired prior information. To achieve this, we design a three-step pipeline. First, we build a pixel searching network that can find the most similar pixels in the reference RAW images as prior information. Then, in the second step, we compress the large-scale reference images to about 0.02% of their original size to reduce the searching cost. Finally, in the last step, we develop a neural network reconstructor to reconstruct the high-fidelity RAW images. Our model achieves comparable, and even better, performance than RAW reconstruction methods based on metadata. © 2024 ACM.

关键词： data assimilation

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IMPROVED EFFICIENCY BASED ON LEARNED SACCADE AND CONTINUOUS SCENE reconstruction from FOVEATED VISUAL SAMPLING 12

IMPROVED EFFICIENCY BASED ON LEARNED SACCADE AND CONTINUOUS ...

引用

12th International conference on Learning Representations, ICLR 2024

作者： Liu, Jiayang Bu, Yiming Ts'o, Daniel Qiu, Qinru Department of Electrical Engineering and Computer Science Syracuse University United States SUNY Upstate Medical University United States

High accuracy, low latency and high energy efficiency represent a set of conflicting goals when searching for system solutions for image classification and detection. While high-quality images naturally result in more precise detection and classification, they also result in a heavier computational workload for imaging and processing, reduced camera frame rates, and increased data communication between the camera and processor. Taking inspiration from the foveal-peripheral sampling mechanism, and saccade mechanism of the human visual system and the filling-in phenomena of brain, we have developed an active scene reconstruction architecture based on multiple foveal views. This model stitches together information from a sequence of foveal-peripheral views, which are sampled from multiple glances. Assisted by a reinforcement learning-based saccade mechanism, our model reduces the required input pixels by over 90% per frame while maintaining the same level of performance in image recognition as with the original images. We evaluated the effectiveness of our model using the GTSRB dataset and the imageNet dataset. Using an equal number of input pixels, our model demonstrates a 5% higher image recognition accuracy compared to state-of-the-art foveal-peripheral based vision systems. Furthermore, we demonstrate that our foveal sampling/saccadic scene reconstruction model exhibits significantly lower complexity and higher data efficiency during the training phase compared to existing approaches. Code is available at Github. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： image recognition

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HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR image TRANSLATION 31

HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR I...

引用

2024 International conference on image Processing

作者： Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin Monash Univ Malaysia Sch Informat Technol Subang Jaya Malaysia TCS Res Robot & Autonomous Syst Lab Kolkata India Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9798350349405;9798350349399

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are overor under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

关键词： data fusion Self-attention High dynamic range imaging Deep learning Histogram equalization

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Reconstructing Particle Flow images Using Regression Models and Deep Neural Networks 5

Reconstructing Particle Flow Images Using Regression Models ...

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5th International conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Yu, Bingyang Qiu, Yuehan Xia, Junbo Zeng, Zhi Oregon Episcopal School PortlandOR97223 United States Shanghai Jiao Tong University Shanghai200240 China Beijing Jiaotong School Beijing100044 China New York University New York CityNY10003 United States

ISBN: (纸本)9798331530334

This work aims to reconstruct accurate images from multiple images acquired by the Large Hadron Collider detector using advanced machine learning techniques. The importance of this task lies in enhancing the precision of image reconstruction for better analysis in high-energy physics experiments. The training data was divided into two sets: the first set focuses on predicting a true image from a sequence of four images, while the second set addresses the lateral pixel shifts in detected images, which result from deviations in particle trajectories due to magnetic fields. To tackle these challenges, we applied various deep learning models. The Multi-Layer Perceptron (MLP) outperformed others in predicting the true image from the first dataset, while the UNet with deep supervision and nested UNet showed superior performance in correcting the image shifts in the second dataset. We trained all models on GPUs to enhance training efficiency. This study introduces novel deep learning strategies to address image shift and distortion, offering significant advancements in image reconstruction tasks for high-energy physics research. © 2024 IEEE.

关键词： image reconstruction

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