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Found 25 papers, 8 papers with code
Seeing Motion at Nighttime with an Event Camera
In this work, we present a novel nighttime dynamic imaging method with an event camera.
Powerful Lossy Compression for Noisy Images
Image compression and denoising represent fundamental challenges in image processing with many real-world applications.
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection
In spatial reasoning stage, we project the compensated events into the same image coordinate, discretize the timestamp of events to obtain a time image that can reflect the motion confidence, and further segment the moving object through adaptive threshold on the time image.
Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow
In this work, we bring the event as a bridge between RGB and LiDAR, and propose a novel hierarchical visual-motion fusion framework for scene flow, which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation.
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space.
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
Learning-based image deraining methods have made great progress.
1st Solution Places for CVPR 2023 UG$^2$+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence
We subsequently select the sharpest set of registered frames by employing a frame selection approach based on image sharpness, and average them to produce an image that is largely free of geometric distortion, albeit with blurriness.
1st Solution Places for CVPR 2023 UG$^{\textbf{2}}$+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence
In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2. 1 of the CVPR 2023 UG$^{2}$+ challenge.
Make Lossy Compression Meaningful for Low-Light Images
Experimental results show that our proposed joint solution achieves a significant improvement over different combinations of existing state-of-the-art sequential ``Compress before Enhance'' or ``Enhance before Compress'' solutions for low-light images, which would make lossy low-light image compression more meaningful.
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3
Secondly, a transformer-based single image deraining network Uformer is implemented to pre-train on large real rain dataset and then fine-tuned on pseudo GT to further improve image restoration.
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
In motion adaptation, we utilize the flow consistency knowledge to align the cross-domain optical flows into a motion-invariance common space, where the optical flow from clean weather is used as the guidance-knowledge to obtain a preliminary optical flow for adverse weather.
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation.
Both Diverse and Realism Matter: Physical Attribute and Style Alignment for Rainy Image Generation
To solve this dilemma, we propose a physical alignment and controllable generation network (PCGNet) for diverse and realistic rain generation.
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.
High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
We implement the IAT in a mathematical invertible manner on a single rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors.
Category-Aware Transformer Network for Better Human-Object Interaction Detection
Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield better performance.
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Consequently, we design an optimization model-driven deep CNN in which the unsupervised loss function of the optimization model is enforced on the proposed network for better generalization.
Effective Actor-centric Human-object Interaction Detection
Moreover, we also use an actor branch to get interaction prediction of the actor and propose a novel composition strategy based on center-point indexing to generate the final HOI prediction.
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal
Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components.
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images.
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts.
Transformed Low-Rank Model for Line Pattern Noise Removal
This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on.
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and spectral correlation are simultaneously captured by 3-order tensors.
Ranked #12 on
Hyperspectral Image Denoising
on ICVL-HSI-Gaussian50
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising
Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising.
