Search Results for author:
Found 40 papers, 19 papers with code
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
To collect our dataset, we build an image acquisition system to simultaneously capture geometrically aligned pairs of blurred and sharp images, and develop a postprocessing method to produce high-quality ground truth images.
Generalizable Novel-View Synthesis using a Stereo Camera
In this paper, we propose the first generalizable view synthesis approach that specifically targets multi-view stereo-camera images.
Gyro-based Neural Single Image Deblurring
To handle gyro error, GyroDeblurNet is equipped with two novel neural network blocks: a gyro refinement block and a gyro deblurring block.
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment.
Task-Oriented Diffusion Model Compression
As recent advancements in large-scale Text-to-Image (T2I) diffusion models have yielded remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) applications have emerged.
UGPNet: Universal Generative Prior for Image Restoration
UGPNet first restores the image structure of a degraded input using a regression model and synthesizes a perceptually-realistic image with a generative model on top of the regressed output.
ParamISP: Learned Forward and Inverse ISPs using Camera Parameters
RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs.
Deep Hybrid Camera Deblurring
We simultaneously capture a long exposure wide-angle image and ultra-wide burst images from a smartphone, and use the sharp burst to estimate blur kernels for deblurring the wide-angle image.
360$^\circ$ Reconstruction From a Single Image Using Space Carved Outpainting
We introduce POP3D, a novel framework that creates a full $360^\circ$-view 3D model from a single image.
SideGAN: 3D-Aware Generative Model for Improved Side-View Image Synthesis
In this paper, we propose SideGAN, a novel 3D GAN training method to generate photo-realistic images irrespective of the camera pose, especially for faces of side-view angles.
ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images
We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images.
Neural Spectro-polarimetric Fields
Here, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength.
Migrant Laborer's Optimization Mechanism Under Employment Permit System(EPS): Introducing and Analyzing 'Skill-Relevance-Self Selection' Model
Analyzing the dynamics between variables at SPNE state, the attained stylized facts are what as follows; [1]Host nation's skill-relevance and wage differential have positive correlation.
Intranational Skill-relevance Model of the Immigrant's Self-selection: Further Evidence of the Stylized Fact from the E-9 Employment Permit System (EPS)
This study expands upon the foundation of 'Skill-Relevance-Self Selection' model on labor immigration, introduced by our previous study (Lee, Lim, & Cho, 2022).
Human Pose Estimation in Extremely Low-Light Conditions
We study human pose estimation in extremely low-light images.
3D-Aware Generative Model for Improved Side-View Image Synthesis
In this paper, we propose SideGAN, a novel 3D GAN training method to generate photo-realistic images irrespective of the camera pose, especially for faces of side-view angles.
Dr.3D: Adapting 3D GANs to Artistic Drawings
To tackle this, we present Dr. 3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings.
DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains
In this paper, we propose DynaGAN, a novel few-shot domain-adaptation method for multiple target domains.
BigColor: Colorization using a Generative Color Prior for Natural Images
In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the-wild images with complex structures.
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data
To address the aforementioned challenge, we propose a novel diffusion strategy of the machine learning (ML) model (FedDif) to maximize the FL performance with non-IID data.
Real-Time Video Deblurring via Lightweight Motion Compensation
While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead.
Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
To facilitate the fusion and propagation of temporal reference features, we propose a propagative temporal fusion module.
Ranked #1 on
Reference-based Video Super-Resolution
on RealMCVSR
Reference-based Video Super-Resolution
Video Super-Resolution
MSSNet: Multi-Scale-Stage Network for Single Image Deblurring
Based on the analysis, we propose Multi-Scale-Stage Network (MSSNet), a novel deep learning-based approach to single image deblurring that adopts our remedies to the defects.
Ranked #4 on
Deblurring
on RealBlur-R
Realistic Blur Synthesis for Learning Image Deblurring
To this end, we present RSBlur, a novel dataset with real blurred images and the corresponding sharp image sequences to enable a detailed analysis of the difference between real and synthetic blur.
Ranked #1 on
Deblurring
on RSBlur (trained on synthetic)
3D Scene Painting via Semantic Image Synthesis
Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene.
CTRL-C: Camera calibration TRansformer with Line-Classification
In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which directly estimates the camera parameters from an image and a set of line segments.
Iterative Filter Adaptive Network for Single Image Defocus Deblurring
We propose a novel end-to-end learning-based approach for single image defocus deblurring.
Ranked #3 on
Image Defocus Deblurring
on RealDOF
Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes
To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames.
Realistic Image Synthesis with Configurable 3D Scene Layouts
Our approach takes a 3D scene with semantic class labels as input and trains a 3D scene painting network that synthesizes color values for the input 3D scene.
GAN Inversion for Out-of-Range Images with Geometric Transformations
For successful semantic editing of real images, it is critical for a GAN inversion method to find an in-domain latent code that aligns with the domain of a pre-trained GAN model.
Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only the scale changes.
Ranked #8 on
Image Defocus Deblurring
on DPD
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Our model encodes individual representations of content (scene structure) and style (artistic appearance) from both source and target images.
Ranked #1 on
Domain Adaptation
on MNIST-to-MNIST-M
Deep Color Transfer using Histogram Analogy
We propose a novel approach to transferring the color of a reference image to a given source image.
URIE: Universal Image Enhancement for Visual Recognition in the Wild
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world.
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries.
Image Classification
Image-level Supervised Instance Segmentation
+2
SRFeat: Single Image Super-Resolution with Feature Discrimination
Generative adversarial networks (GANs) have recently been adopted to single image super resolution (SISR) and showed impressive results with realistically synthesized high-frequency textures.
Autonomous Power Allocation based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network
For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner.
Convergence Analysis of MAP based Blur Kernel Estimation
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image.
Deblurring Low-light Images with Light Streaks
Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time.
Ranked #11 on
Deblurring
on RealBlur-R (trained on GoPro)
Handling Noise in Single Image Deblurring Using Directional Filters
Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image.
