Search Results for author:
Found 32 papers, 14 papers with code
Learning Event-Driven Video Deblurring and Interpolation
Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy.
Learning to See in the Dark with Events
A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss.
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution
In practice, inspired by the fact that image super-resolution and segmentation can benefit each other, we propose SegSR which introduces a dual-diffusion framework to facilitate interaction between the image super-resolution and segmentation diffusion models.
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map.
Deep Dynamic Scene Deblurring from Optical Flow
In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN).
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health.
Efficient Deep Image Denoising via Class Specific Convolution
In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification.
Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric
To answer the questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing ALgorithms (PIPAL) dataset.
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration
To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset.
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection
Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion.
Learning Event-Based Motion Deblurring
Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process.
Ranked #35 on
Image Deblurring
on GoPro
(using extra training data)
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
In this work, we show that the coupled EMA teacher causes a performance bottleneck.
Semi-Supervised Image Classification
Unsupervised Domain Adaptation
Image Formation Model Guided Deep Image Super-Resolution
We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution.
Spatio-Temporal Filter Adaptive Network for Video Deblurring
To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework.
Ranked #3 on
Deblurring
on DVD
(using extra training data)
DAVANet: Stereo Deblurring with View Aggregation
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles.
Very Power Efficient Neural Time-of-Flight
Therefore, the design of a power efficient ToF camera always creates a painful dilemma for the illumination and the performance trade-off.
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
We present an algorithm to directly solve numerous image restoration problems (e. g., image deblurring, image dehazing, image deraining, etc.).
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects.
Ranked #3 on
Visual Social Relationship Recognition
on PIPA
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN).
Ranked #11 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Learning Dual Convolutional Neural Networks for Low-Level Vision
These problems usually involve the estimation of two components of the target signals: structures and details.
Learning Selfie-Friendly Abstraction from Artistic Style Images
Artistic style transfer can be thought as a process to generate different versions of abstraction of the original image.
3D Human Pose Estimation in the Wild by Adversarial Learning
Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.
Single View Stereo Matching
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.
Ranked #49 on
Monocular Depth Estimation
on KITTI Eigen split
LSTM Pose Machines
Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e. g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames.
Ranked #3 on
Pose Estimation
on J-HMDB
Image Dehazing using Bilinear Composition Loss Function
In this paper, we introduce a bilinear composition loss function to address the problem of image dehazing.
Robust Tracking Using Region Proposal Networks
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable.
Deep Multimodal Speaker Naming
Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video.
