Image Reconstruction
525 papers with code • 5 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Image Reconstruction
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Libraries
Use these libraries to find Image Reconstruction models and implementations
General-100
CED
SEN12MS-CR
SEN12MS-CR-TS
Latest papers
SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back
We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of fMRI acquisition techniques. Unlike existing tools, the goal here is to simulate the complete chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various multi-coil k-space data 3D sampling strategies, with the possibility of extending the forward acquisition model to various noise and artifact sources while remaining memory-efficient. By using this \emph{in silico} setup, we are thus able to provide realistic and reproducible ground truth for fMRI reconstruction methods in 3D accelerated acquisition settings and explore the influence of critical parameters, such as the acceleration factor and signal-to-noise ratio~(SNR), on downstream tasks of image reconstruction and statistical analysis of evoked brain activity. We present three scenarios of increasing complexity to showcase the flexibility, versatility, and fidelity of \emph{SNAKE-fMRI}: From a temporally-fixed full 3D Cartesian to various 3D non-Cartesian sampling patterns, we can compare -- with reproducibility guarantees -- how experimental paradigms, acquisition strategies and reconstruction methods contribute and interact together, affecting the downstream statistical analysis.
MindBridge: A Cross-Subject Brain Decoding Framework
Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained.
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography
To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT.
Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction
Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice.
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
To better explore the common degradation representations from spatially-varying rain streaks, we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design, enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios.
Image Reconstruction from Electroencephalography Using Latent Diffusion
desa-lab/eeg-image-reconstruction
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In this work, we have adopted the diffusion-based image reconstruction pipeline previously used for fMRI image reconstruction and applied it to Electroencephalography (EEG).
Learning to Rank Patches for Unbiased Image Redundancy Reduction
irslu/ltrp
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The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content.
SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image
wu-cvgl/scinerf
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SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors.
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
High Dynamic Range (HDR) content (i. e., images and videos) has a broad range of applications.
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images
songyxing/idf-cr
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IDF-CR consists of a pixel space cloud removal module (Pixel-CR) and a latent space iterative noise diffusion network (IND).
