Search Results for author:
Found 17 papers, 9 papers with code
Deep Bilateral Learning for Real-Time Image Enhancement
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
Burst Denoising with Kernel Prediction Networks
We present a technique for jointly denoising bursts of images taken from a handheld camera.
Unprocessing Images for Learned Raw Denoising
Machine learning techniques work best when the data used for training resembles the data used for evaluation.
Ranked #1 on
Color Image Denoising
on Darmstadt Noise Dataset
Wireless Software Synchronization of Multiple Distributed Cameras
We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi.
Stereoscopic Dark Flash for Low-light Photography
In this work, we present a camera configuration for acquiring "stereoscopic dark flash" images: a simultaneous stereo pair in which one camera is a conventional RGB sensor, but the other camera is sensitive to near-infrared and near-ultraviolet instead of R and B.
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera.
Real-time Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism.
How to Train Neural Networks for Flare Removal
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts.
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT
Virtual Reality (VR) has shown great potential to revolutionize the market by providing users immersive experiences with freedom of movement.
Networking and Internet Architecture
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.
The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement
Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates.
Splatting-based Synthesis for Video Frame Interpolation
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence.
Self-Supervised Burst Super-Resolution
Extensive experiments on real and synthetic data show that, despite only using noisy bursts during training, models trained with our self-supervised strategy match, and sometimes surpass, the quality of fully-supervised baselines trained with synthetic data or weakly-paired ground-truth.
MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction
The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets.
Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making.
Learning Lens Blur Fields
Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements.
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
Our study reveals that instruction controllable text summarization remains a challenging task for LLMs, since (1) all LLMs evaluated still make factual and other types of errors in their summaries; (2) all LLM-based evaluation methods cannot achieve a strong alignment with human annotators when judging the quality of candidate summaries; (3) different LLMs show large performance gaps in summary generation and evaluation.
