Search Results for author:
Found 23 papers, 10 papers with code
Harnessing Data and Physics for Deep Learning Phase Recovery
Two main deep learning phase recovery strategies are data-driven (DD) with supervised learning mode and physics-driven (PD) with self-supervised learning mode.
SASA: Saliency-Aware Self-Adaptive Snapshot Compressive Imaging
The ability of snapshot compressive imaging (SCI) systems to efficiently capture high-dimensional (HD) data depends on the advent of novel optical designs to sample the HD data as two-dimensional (2D) compressed measurements.
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising
Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result.
Neuromorphic Imaging and Classification with Graph Learning
Bio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams.
On the use of deep learning for phase recovery
Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing.
Improving Video Colorization by Test-Time Tuning
With the advancements in deep learning, video colorization by propagating color information from a colorized reference frame to a monochrome video sequence has been well explored.
Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging
Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range.
Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction
Depth estimation from light field (LF) images is a fundamental step for numerous applications.
PIDRo: Parallel Isomeric Attention with Dynamic Routing for Text-Video Retrieval
The parallel isomeric attention module is used as the video encoder, which consists of two parallel branches modeling the spatial-temporal information of videos from both patch and frame levels.
Ranked #3 on
Video Retrieval
on MSR-VTT-1kA
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images
Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging.
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator
While a few previous studies tried to automatically generate 3D bounding boxes from weak labels such as 2D boxes, the quality is sub-optimal compared to human annotators.
Point Cloud Denoising via Momentum Ascent in Gradient Fields
To achieve point cloud denoising, traditional methods heavily rely on geometric priors, and most learning-based approaches suffer from outliers and loss of details.
MANet: Improving Video Denoising with a Multi-Alignment Network
In video denoising, the adjacent frames often provide very useful information, but accurate alignment is needed before such information can be harnassed.
An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging
Imaging under photon-scarce situations introduces challenges to many applications as the captured images are with low signal-to-noise ratio and poor luminance.
Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation
Transfer learning (TL) for medical image segmentation helps deep learning models achieve more accurate performances when there are scarce medical images.
Cross-Camera Human Motion Transfer by Time Series Analysis
With advances in optical sensor technology, heterogeneous camera systems are increasingly used for high-resolution (HR) video acquisition and analysis.
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.
High-Order Residual Network for Light Field Super-Resolution
In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction.
High-dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction
We consider the problem of high-dimensional light field reconstruction and develop a learning-based framework for spatial and angular super-resolution.
Image Reconstruction Using Deep Learning
This paper proposes a deep learning architecture that attains statistically significant improvements over traditional algorithms in Poisson image denoising espically when the noise is strong.
Fast and robust misalignment correction of Fourier ptychographic microscopy
Fourier ptychographi cmicroscopy(FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV).
Analysis of the noise in back-projection light field acquisition and its optimization
In this paper, we analyze the defocus noise and the depth resolution in the focal plane sweeping based light field reconstruction technique, and propose a method to reduce the defocus noise and improve the depth resolution.
Consistency Analysis for the Doubly Stochastic Dirichlet Process
This technical report proves components consistency for the Doubly Stochastic Dirichlet Process with exponential convergence of posterior probability.
