Search Results for author:
Found 85 papers, 56 papers with code
SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization
We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization.
Direct Photometric Alignment by Mesh Deformation
The choice of motion models is vital in applications like image/video stitching and video stabilization.
MGANet: A Robust Model for Quality Enhancement of Compressed Video
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth.
Removing rain streaks by a linear model
Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems.
Rain Removal By Image Quasi-Sparsity Priors
We present a novel rain removal method in this paper, which consists of two steps, i. e., detection of rain streaks and reconstruction of the rain-removed image.
C3AE: Exploring the Limits of Compact Model for Age Estimation
Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.
Ranked #3 on
Age Estimation
on FGNET
Content-Aware Unsupervised Deep Homography Estimation
Homography estimation is a basic image alignment method in many applications.
Ranked #5 on
Homography Estimation
on S-COCO
DeepBlindness: Fast Blindness Map Estimation and Blindness Type Classification for Outdoor Scene from Single Color Image
Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues".
Neural Point Cloud Rendering via Multi-Plane Projection
The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning ability
The unprecedented performance achieved by deep convolutional neural networks for image classification is linked primarily to their ability of capturing rich structural features at various layers within networks.
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration
Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance.
SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping
The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources.
Adversarial Imitation Attack
Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.
DaST: Data-free Substitute Training for Adversarial Attacks
In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data.
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
Occlusion is an inevitable and critical problem in unsupervised optical flow learning.
Decision-based Universal Adversarial Attack
A single perturbation can pose the most natural images to be misclassified by classifiers.
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on
Optical Flow Estimation
on Sintel Final unsupervised
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #1 on
Image Denoising
on SIDD
(SSIM (sRGB) metric)
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks
We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders.
DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation
In this work, we propose a deep network that compensates the motions caused by the OIS, such that the gyroscopes can be used for image alignment on the OIS cameras.
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data
The ground truth images play a leading role in generating reasonable HDR images.
OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration
On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features.
Holistic 3D Scene Understanding from a Single Image with Implicit Representation
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
Ranked #1 on
Monocular 3D Object Detection
on SUN RGB-D
(using extra training data)
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
Experiments show that our method outperforms the state-of-art methods in both regular and challenging scenes.
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage.
Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection
Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped.
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Practical Wide-Angle Portraits Correction with Deep Structured Models
Wide-angle portraits often enjoy expanded views.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
Ranked #5 on
Face Alignment
on WFW (Extra Data)
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration
Data association is important in the point cloud registration.
EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR).
Ranked #2 on
Burst Image Super-Resolution
on SyntheticBurst
Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images
Even compared with the supervised solutions, our image stitching quality is still preferred by users.
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration.
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization
Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods.
Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer
We propose a semi-supervised network for wide-angle portraits correction.
SceneSqueezer: Learning To Compress Scene for Camera Relocalization
Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image.
Learning Optical Flow With Kernel Patch Attention
Optical flow is a fundamental method used for quantitative motion estimation on the image plane.
Learning To Zoom Inside Camera Imaging Pipeline
To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model.
Learning Optical Flow with Adaptive Graph Reasoning
Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph.
Deep Constrained Least Squares for Blind Image Super-Resolution
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
Ranked #1 on
Blind Super-Resolution
on DIV2KRK - 2x upscaling
Deep Rectangling for Image Stitching: A Learning Baseline
In this paper, we address these issues by proposing the first deep learning solution to image rectangling.
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction.
Ranked #1 on
Burst Image Super-Resolution
on BurstSR
Unsupervised Homography Estimation with Coplanarity-Aware GAN
Estimating homography from an image pair is a fundamental problem in image alignment.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Thunder: Thumbnail based Fast Lightweight Image Denoising Network
To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Deep Rotation Correction without Angle Prior
To this end, we leverage a neural network to predict the optical flows that can warp the tilted images to be perceptually horizontal.
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow.
Ghost-free High Dynamic Range Imaging with Context-aware Transformer
High dynamic range (HDR) deghosting algorithms aim to generate ghost-free HDR images with realistic details.
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
The proposed nearest convolution has the same performance as the nearest upsampling but is much faster and more suitable for Android NNAPI.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Minimum Latency Deep Online Video Stabilization
In this work, we adopt recent off-the-shelf high-quality deep motion models for motion estimation to recover the camera trajectory and focus on the latter two steps.
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field.
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging
Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function.
DAS: Neural Architecture Search via Distinguishing Activation Score
NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer.
Explicit Motion Disentangling for Efficient Optical Flow Estimation
In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency.
Deep Homography Mixture for Single Image Rolling Shutter Correction
In this work, we adopt a more straightforward method to learn deep homography mixture motion between an RS image and its corresponding GS image, without large solution space or strict restrictions on image features.
SIRA-PCR: Sim-to-Real Adaptation for 3D Point Cloud Registration
We design SIRA-PCR, a new approach to 3D point cloud registration.
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning
Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details.
Parallax-Tolerant Unsupervised Deep Image Stitching
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
Learning Optical Flow from Event Camera with Rendered Dataset
Previous datasets are created by either capturing real scenes by event cameras or synthesizing from images with pasted foreground objects.
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch
The method combines the efficiency of optical flow and the accuracy of PatchMatch.
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance.
Realistic Noise Synthesis with Diffusion Models
Deep image denoising models often rely on large amount of training data for the high quality performance.
Low-Light Image Enhancement with Wavelet-based Diffusion Models
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Ranked #1 on
Low-Light Image Enhancement
on LOLv2
Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions
On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data.
SAM-IQA: Can Segment Anything Boost Image Quality Assessment?
Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks.
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
Supervised Homography Learning with Realistic Dataset Generation
In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network.
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network
We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination.
AccFlow: Backward Accumulation for Long-Range Optical Flow
To train and evaluate the proposed AccFlow, we have constructed a large-scale high-quality dataset named CVO, which provides ground-truth optical flow labels between adjacent and distant frames.
MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
In this paper, we introduce a new approach for high-quality multi-exposure image fusion (MEF).
GAFlow: Incorporating Gaussian Attention into Optical Flow
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field
We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output.
Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond
To break this bottleneck, we propose the coupled thin-plate spline model (CoupledTPS), which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation.
Eliminating Warping Shakes for Unsupervised Online Video Stitching
In this paper, we retarget video stitching to an emerging issue, named warping shake, when extending image stitching to video stitching.
Multiple Latent Space Mapping for Compressed Dark Image Enhancement
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions
Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets.
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Image stitching from different captures often results in non-rectangular boundaries, which is often considered unappealing.
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation
In this paper, we address the Bracket Image Restoration and Enhancement (BracketIRE) task using a novel framework, which requires restoring a high-quality high dynamic range (HDR) image from a sequence of noisy, blurred, and low dynamic range (LDR) multi-exposure RAW inputs.
