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Found 13 papers, 10 papers with code
Emotion Recognition Using Transformers with Masked Learning
In recent years, deep learning has achieved innovative advancements in various fields, including the analysis of human emotions and behaviors.
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.
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.
Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator
Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher.
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
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)
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization
Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment.
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.
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
Deep Color Transfer using Histogram Analogy
We propose a novel approach to transferring the color of a reference image to a given source image.
Deep Defocus Map Estimation Using Domain Adaptation
Our method is evaluated on publicly available blur detection and blur estimation datasets and the results show the state-of-the-art performance. In this paper, we propose the first end-to-end convolutional neural network (CNN) architecture, Defocus Map Estimation Network (DMENet), for spatially varying defocus map estimation.
Ranked #1 on
Defocus Estimation
on CUHK - Blur Detection Dataset
