Search Results for author:
Found 29 papers, 14 papers with code
Event-Enhanced Snapshot Compressive Videography at 10K FPS
To unlock the potential of conventional snapshot compressive videography, we propose a novel hybrid "intensity+event" imaging scheme by incorporating an event camera into a video SCI setup.
An Event-Oriented Diffusion-Refinement Method for Sparse Events Completion
Event cameras or dynamic vision sensors (DVS) record asynchronous response to brightness changes instead of conventional intensity frames, and feature ultra-high sensitivity at low bandwidth.
A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression.
Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos
In this work, by leveraging classical coded exposure imaging technique and emerging implicit neural representation for videos, we tactfully embed the motion direction cues into the blurry image during the imaging process and develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image utilizing the embedded motion direction cues.
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation.
HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field
The problem of modeling an animatable 3D human head avatar under light-weight setups is of significant importance but has not been well solved.
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions.
SHoP: A Deep Learning Framework for Solving High-order Partial Differential Equations
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Causal discovery from time-series data has been a central task in machine learning.
DarkVision: A Benchmark for Low-light Image/Video Perception
In this paper, we contribute the first multi-illuminance, multi-camera, and low-light dataset, named DarkVision, serving for both image enhancement and object detection.
TINC: Tree-structured Implicit Neural Compression
In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner.
SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of INR based compressor.
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions
In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring.
A Dual Sensor Computational Camera for High Quality Dark Videography
In this paper, we propose to build a dual-sensor camera to additionally collect the photons in NIR wavelength, and make use of the correlation between RGB and near-infrared (NIR) spectrum to perform high-quality reconstruction from noisy dark video pairs.
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision.
10-mega pixel snapshot compressive imaging with a hybrid coded aperture
Towards this end, snapshot compressive imaging (SCI) was proposed as a promising solution to improve the throughput of imaging systems by compressive sampling and computational reconstruction.
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution
High quality imaging usually requires bulky and expensive lenses to compensate geometric and chromatic aberrations.
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments.
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot.
Rank Minimization for Snapshot Compressive Imaging
We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction.
Experimental comparison of single-pixel imaging algorithms
Various algorithms have been proposed for SPI reconstruction, including the linear correlation methods, the alternating projection method (AP), and the compressive sensing based methods.
Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient
Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging.
Fast and High Quality Highlight Removal from A Single Image
Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively.
Scene-adaptive Coded Apertures Imaging
Firstly, we capture the scene with a pinhole and analyze the scene content to determine primary edge orientations.
Blind Optical Aberration Correction by Exploring Geometric and Visual Priors
Furthermore, by investigating the visual artifacts of aberration degenerated images captured by consumer-level cameras, the non-uniform distribution of sharpness across color channels and the image lattice is exploited as visual priors, resulting in a novel strategy to utilize the guidance from the sharpest channel and local image regions to improve the overall performance and robustness.
Transparent Object Reconstruction via Coded Transport of Intensity
Capturing and understanding visual signals is one of the core interests of computer vision.
Multi-frame denoising of high speed optical coherence tomography data using inter-frame and intra-frame priors
Optical coherence tomography (OCT) is an important interferometric diagnostic technique which provides cross-sectional views of the subsurface microstructure of biological tissues.
