Papers Image Reconstruction
“Image Reconstruction” 태그가 달린 논문 2,464편 · 필터 해제
DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence
High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity and generation efficiency: high compres…
Text-to-Image GenerationImage ReconstructionOn the Diffusibility of High-Dimensional Latents
Representation Autoencoders (RAEs) enable diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fi…
Text-to-Image GenerationImage ReconstructionHybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction
Variational quantum circuits (VQCs) are parameterized quantum circuits optimized classically. We propose a hybrid variational quantum circuit (HVQC) extending VQCs with a classical affine post-measurement layer, enabling…
Image ReconstructionPhysics-Guided Flow Matching for CT Image Reconstruction
Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruction performance. However, diffusion mode…
Computational EfficiencyImage ReconstructionData AugmentationGAN-Based Semantic Communication for Image Transmission in IoV
For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional commun…
Semantic CommunicationSemantic SegmentationImage ReconstructionTissue-Mixture Entropy-Weighted Reconstruction for Partial-Volume-Aware Brain MRI Super-Resolution
Full-image objectives in brain magnetic resonance imaging (MRI) super-resolution (SR) can underweight tissue-transition regions affected by the partial-volume effect (PVE), as these regions occupy only a small fraction o…
Image ReconstructionSimplified Cross-Modal Calibration for Heterogeneous Event-RGB Stereo Systems
Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing approaches often require sensor or target motion, precise synchronizati…
Image ReconstructionFlow Matching-Based PET Image Reconstruction
Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated promising performance, they often require…
Image ReconstructionTISC: A Text-Driven Image Semantic Communication System for Faithful Reconstruction
Generative image semantic communication converts an image into a text description and then performs text-to-image reconstruction at the receiver via diffusion-based generative models. This paradigm has attracted broad at…
Semantic CommunicationImage ReconstructionUniSpace: Unified Visual Representation and Scalable Multimodal Modeling
Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation. However, their final tokens discard fine-grained visual details, leading to poor…
Text-to-Image GenerationImage ReconstructionImage EditingE2Pano: Learning Event-to-Panorama Image Reconstruction
Event cameras offer microsecond-level temporal resolution and high dynamic range, potentially facilitating motion-blur-free panoramic imaging from fast rotational scanning. Nonetheless, existing optimization-based method…
Image ReconstructionODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time prediction, thereby exhibiting signific…
Image ReconstructionVideo GenerationThree-Photon Bayesian Imaging of Ortho-Positronium
PET provides functional images relying on two-photon coincidences from positron-electron annihilation. In human tissue, about 40\% of annihilations are preceded by Ps formation, of which o-Ps component partially decays i…
Image ReconstructionConformalized Rate-Adaptive Sensing
Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method t…
Image ReconstructionMatrix-Free Photoacoustic Image Reconstruction via Sensor-Token Self-Attention
Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering the initial pressure distribution from sparse-view sensor measurement…
Image ReconstructionSeeing or Knowing? Visual Context Sensitivity in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) achieve strong performance by integrating visual inputs with the rich priors of pretrained language models. However, they often fail on vision-centric tasks, especially when visua…
Image ReconstructionTrainable Nonexpansive Denoisers for Contractive Image Reconstruction
Trainable denoisers with Lipschitz control have become central to convergent image reconstruction. However, training neural networks that simultaneously offer strong denoising performance and global Lipschitz guarantees …
Image ReconstructionStabilizing Deep Reconstruction Operators with Contractive Anchoring
Pretrained deep denoisers can be used to solve a wide range of model-based image reconstruction tasks via Plug-and-Play (PnP) and Regularization-by-Denoising (RED) algorithms, without retraining per task. These denoisers…
Image ReconstructionscMIR: a vision-language foundation model for single-cell light microscopy image representation
Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis. Existing represen…
Representation LearningImage ReconstructionDomain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction
Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors. In practice, these denoisers are often deployed outside their training domains. Existing anal…
Image ReconstructionDomain Adaptation