Search Results for author:
Found 65 papers, 33 papers with code
Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding
Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignoring semantic information.
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement
Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow.
Omni-Kernel Network for Image Restoration
Extensive experiments demonstrate that our network achieves state-of-the-art performance on 11 benchmark datasets for three representative image restoration tasks, including image dehazing, image desnowing, and image defocus deblurring.
Ranked #8 on
Image Dehazing
on SOTS Outdoor
How Powerful Potential of Attention on Image Restoration?
Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration
Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model's attention to focus on the features more relevant to the degraded regions in need of reconstruction.
Logit Standardization in Knowledge Distillation
Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function.
Ranked #1 on
Knowledge Distillation
on CIFAR-100
MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement
To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML).
Image Restoration via Frequency Selection
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Ranked #2 on
Image Dehazing
on SOTS Outdoor
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus.
Taxonomy Completion with Probabilistic Scorer via Box Embedding
Specifically, TaxBox consists of three components: (1) a graph aggregation module to leverage the structural information of the taxonomy and two lightweight decoders that map features to box embedding and capture complex relationships between concepts; (2) two probabilistic scorers that correspond to attachment and insertion operations and ensure the avoidance of pseudo-leaves; and (3) three learning objectives that assist the model in mapping concepts more granularly onto the box embedding space.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
IRNeXt: Rethinking Convolutional Network Design for Image Restoration
We present IRNeXt, a simple yet effective convolutional network architecture for image restoration.
Ranked #4 on
Image Dehazing
on SOTS Outdoor
SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing
The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details.
Selective Frequency Network for Image Restoration
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
Ranked #1 on
Deblurring
on RSBlur
Robust Single Image Reflection Removal Against Adversarial Attacks
This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks.
Ranked #2 on
Reflection Removal
on Real20
Lightweight Image Super-Resolution with Superpixel Token Interaction
Our method employs superpixels to cluster local similar pixels to form the explicable local regions and utilizes intra-superpixel attention to enable local information interaction.
Focal Network for Image Restoration
Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields.
Ranked #7 on
Image Dehazing
on SOTS Outdoor
Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN
In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes.
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning
In contrast, we present a domain generalization framework based on meta-learning to dig out representative and discriminative internal properties of real hazy domains without test-time training.
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed.
Rethinking Image Restoration for Object Detection
To address the issue, we propose a targeted adversarial attack in the restoration procedure to boost object detection performance after restoration.
Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement
In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase.
Ranked #14 on
Image Dehazing
on SOTS Indoor
Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a seamless cylinder.
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image.
Deep Image Deblurring: A Survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.
MC-Blur: A Comprehensive Benchmark for Image Deblurring
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios.
A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging
We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images.
Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning
To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs.
A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning
With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed.
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
Ranked #7 on
Table Recognition
on PubTabNet
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost.
Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior
We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.
Ranked #2 on
Underwater Image Restoration
on LSUI
(using extra training data)
Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems.
Progressive residual learning for single image dehazing
The recent physical model-free dehazing methods have achieved state-of-the-art performances.
Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal
In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a "heavy-to-light" scheme to remove rain via addressing the unsatisfying deraining regions.
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions.
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
Progressive Depth Learning for Single Image Dehazing
However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase.
Pyramid Architecture Search for Real-Time Image Deblurring
Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes.
Ultra-High-Definition Image HDR Reconstruction via Collaborative Bilateral Learning
First, we propose a dual-path network to extract content and chromatic features at a reduced resolution of the low dynamic range (LDR) input.
Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring
Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e. g., 4K resolution).
Single Image Super-Resolution via a Holistic Attention Network
Informative features play a crucial role in the single image super-resolution task.
Ranked #2 on
Image Super-Resolution
on Urban100 - 8x upscaling
Face Super-Resolution Guided by 3D Facial Priors
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.
Feedback Graph Attention Convolutional Network for Medical Image Enhancement
To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named Feedback Graph Attention Convolutional Network (FB-GACN).
Domain Adaptation for Image Dehazing
By training image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing.
Ranked #5 on
Image Dehazing
on RESIDE-6K
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation
Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions.
Ranked #13 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Face Video Deblurring Using 3D Facial Priors
The model consists of two main branches: i) a face video deblurring sub-network based on an encoder-decoder architecture, and ii) a 3D face reconstruction and rendering branch for predicting 3D priors of salient facial structures and identity knowledge.
UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
Single Image Deraining: A Comprehensive Benchmark Analysis
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.
An Underwater Image Enhancement Benchmark Dataset and Beyond
In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images.
Ranked #5 on
Underwater Image Restoration
on LSUI
(using extra training data)
Unsupervised Degradation Learning for Single Image Super-Resolution
Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle.
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.
Rendering Portraitures from Monocular Camera and Beyond
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Learning to Deblur Images with Exemplars
However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation.
Fast Single Image Rain Removal via a Deep Decomposition-Composition Network
Rain effect in images typically is annoying for many multimedia and computer vision tasks.
Gated Fusion Network for Single Image Dehazing
The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder.
Ranked #23 on
Image Dehazing
on SOTS Outdoor
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel
We analyze the relationship between motion blur trajectory and optical flow, and present a novel pixel-wise non-linear kernel model to account for motion blur.
Image Deblurring via Extreme Channels Prior
The proposed method takes advantage of both Bright and Dark Channel Prior.
SketchNet: Sketch Classification With Web Images
In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images.
