Search Results for author:
Found 39 papers, 9 papers with code
Reflection Separation via Multi-bounce Polarization State Tracing
Through a combination of a new polarization-guided image formation model and a novel supervised learning framework for the interpretation of a ray-tracing polarized image formation model, a general method is obtained to tackle general image reflection removal problems.
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction
Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows.
Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification
Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements.
Aberration-Aware Depth-from-Focus
Computer vision methods for depth estimation usually use simple camera models with idealized optics.
Curriculum Learning for ab initio Deep Learned Refractive Optics
Deep lens optimization has recently emerged as a new paradigm for designing computational imaging systems, however it has been limited to either simple optical systems consisting of a single DOE or metalens, or the fine-tuning of compound lenses from good initial designs.
CRISPnet: Color Rendition ISP Net
In recent years there has been strong interest in replacing the historically grown ISP systems with deep learned pipelines.
Neural Adaptive SCEne Tracing
Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost.
NeAT: Neural Adaptive Tomography
Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods.
Snapshot HDR Video Construction Using Coded Mask
This paper study the reconstruction of High Dynamic Range (HDR) video from snapshot-coded LDR video.
ISP-Agnostic Image Reconstruction for Under-Display Cameras
To obtain large quantities of real under-display camera training data with sufficient contrast and scene diversity, we furthermore develop a data capture method utilizing an HDR monitor, as well as a data augmentation method to generate suitable HDR content.
Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering
Simultaneous reconstruction of geometry and reflectance properties in uncontrolled environments remains a challenging problem.
Diffractive lensless imaging with optimized Voronoi-Fresnel phase
Here we report a diffractive lensless camera with spatially-coded Voronoi-Fresnel phase to achieve superior image quality.
Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display
As a result, modern holographic displays possess low \'{e}tendue, which is the product of the display area and the maximum solid angle of diffracted light.
Seeing in Extra Darkness Using a Deep-Red Flash
We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source.
Mask-ToF: Learning Microlens Masks for Flying Pixel Correction in Time-of-Flight Imaging
We introduce Mask-ToF, a method to reduce flying pixels (FP) in time-of-flight (ToF) depth captures.
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces
We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water.
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and Prediction
After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors.
Multispectral Illumination Estimation Using Deep Unrolling Network
This paper examines the problem of illumination spectra estimation in multispectral images.
Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics
Imaging depth and spectrum have been extensively studied in isolation from each other for decades.
Stochastic Convolutional Sparse Coding
In this work, we propose a novel stochastic spatial-domain solver, in which a randomized subsampling strategy is introduced during the learning sparse codes.
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Super-Resolution and Sparse View CT Reconstruction
As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator.
Coupled Fluid Density and Motion from Single Views
We present a novel method to reconstruct a fluid's 3D density and motion based on just a single sequence of images.
Deep End-to-End Time-of-Flight Imaging
We present an end-to-end image processing framework for time-of-flight (ToF) cameras.
Depth and Transient Imaging With Compressive SPAD Array Cameras
Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest.
Imaging with SPADs and DMDs: Seeing through Diffraction-Photons
This paper addresses the problem of imaging in the presence of diffraction-photons.
Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction
Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules.
Consensus Convolutional Sparse Coding
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision.
Deep Video Deblurring for Hand-Held Cameras
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency.
Deep Video Deblurring
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms
We present a novel algorithm for blind denoising of images corrupted by mixed impulse, Poisson, and Gaussian noises.
TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications
We present TRex, a flexible and robust Tomographic Reconstruction framework using proximal algorithms.
Material Classification Using Raw Time-Of-Flight Measurements
We propose a material classification method using raw time-of-flight (ToF) measurements.
Fast and Flexible Convolutional Sparse Coding
Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision.
Defocus Deblurring and Superresolution for Time-of-Flight Depth Cameras
Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications.
Rolling Shutter Motion Deblurring
Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur.
Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors
The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light.
Stochastic Deconvolution
We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks.
