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Found 100 papers, 53 papers with code
Image Super-Resolution Using Deep Convolutional Networks
We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Ranked #2 on
Video Super-Resolution
on Xiph HD - 4x upscaling
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).
Ranked #2 on
Face Hallucination
on FFHQ 512 x 512 - 16x upscaling
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
Ranked #2 on
Deblurring
on REDS
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension.
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
In this paper, we show that it is possible to recover textures faithful to semantic classes.
Ranked #55 on
Image Super-Resolution
on BSD100 - 4x upscaling
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Ranked #1 on
Blind Face Restoration
on LFW
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Activating More Pixels in Image Super-Resolution Transformer
In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement.
Ranked #1 on
Image Super-Resolution
on Set5 - 2x upscaling
HAT: Hybrid Attention Transformer for Image Restoration
In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
Enhanced Quadratic Video Interpolation
In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model.
Efficient Image Super-Resolution Using Pixel Attention
Pixel attention (PA) is similar as channel attention and spatial attention in formulation.
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Equipped with the VQ codebook as a facial detail dictionary and the parallel decoder design, the proposed VQFR can largely enhance the restored quality of facial details while keeping the fidelity to previous methods.
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of perceptual metrics.
Ranked #1 on
Image Super-Resolution
on PIRM-test
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
We introduce a Depicted image Quality Assessment method (DepictQA), overcoming the constraints of traditional score-based methods.
Masked Image Training for Generalizable Deep Image Denoising
To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training.
Accelerating the Super-Resolution Convolutional Neural Network
As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality.
Blind Super-Resolution With Iterative Kernel Correction
In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown.
Ranked #2 on
Blind Super-Resolution
on Set5 - 3x upscaling
Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning
We investigate a novel approach for image restoration by reinforcement learning.
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.
Ranked #2 on
Color Image Denoising
on CBSD68 sigma75
Blueprint Separable Residual Network for Efficient Image Super-Resolution
One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation.
HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization
In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization.
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution
Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner.
Conditional Sequential Modulation for Efficient Global Image Retouching
The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector.
A New Journey from SDRTV to HDRTV
However, most available resources are still in standard dynamic range (SDR).
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
However, the majority of available resources are still in standard dynamic range (SDR).
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks.
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.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data.
Rethinking Alignment in Video Super-Resolution Transformers
In this paper, we rethink the role of alignment in VSR Transformers and make several counter-intuitive observations.
Ranked #2 on
Video Super-Resolution
on Vid4 - 4x upscaling
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels.
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies
Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations.
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
In this work, we design an efficient SR network by improving the attention mechanism.
Mitigating Artifacts in Real-World Video Super-Resolution Models
To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated.
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences.
Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy
While single task image restoration (IR) has achieved significant successes, it remains a challenging issue to train a single model which can tackle multiple IR tasks.
Compression Artifacts Reduction by a Deep Convolutional Network
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
DegAE: A New Pretraining Paradigm for Low-Level Vision
However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging.
Path-Restore: Learning Network Path Selection for Image Restoration
To leverage this, we propose Path-Restore, a multi-path CNN with a pathfinder that can dynamically select an appropriate route for each image region.
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
We address this problem from a new perspective, by jointly considering colorization and temporal consistency in a unified framework.
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
In particular, we cluster the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set.
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network
Particularly, flare and blur in UDC images could severely deteriorate the user experience in high dynamic range (HDR) scenes.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Deep Convolution Networks for Compression Artifacts Reduction
Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring.
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner.
Deep Network Interpolation for Continuous Imagery Effect Transition
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect.
Modeling Rich Contexts for Sentiment Classification with LSTM
Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task.
Boosting Optical Character Recognition: A Super-Resolution Approach
Text image super-resolution is a challenging yet open research problem in the computer vision community.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function
Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.
Suppressing Model Overfitting for Image Super-Resolution Networks
Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved.
Attentive Representation Learning with Adversarial Training for Short Text Clustering
Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model.
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.
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding to the VSR techniques based on deep learning.
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
Multi-scale techniques have achieved great success in a wide range of computer vision tasks.
Understanding Deformable Alignment in Video Super-Resolution
Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.
Interpreting Super-Resolution Networks with Local Attribution Maps
Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance.
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.
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning in 6G Networks
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices.
Networking and Internet Architecture
Graph-Based Tri-Attention Network for Answer Ranking in CQA
However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked.
Very Lightweight Photo Retouching Network with Conditional Sequential Modulation
In practice, photo retouching can be accomplished by a series of image processing operations.
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields.
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods.
Toward Interactive Modulation for Photo-Realistic Image Restoration
The base networks comprise a generator and a discriminator.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Blind Image Super-Resolution: A Survey and Beyond
This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used for solving the SR model.
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics.
Discovering Distinctive "Semantics" in Super-Resolution Networks
We show that a well-trained deep SR network is naturally a good descriptor of degradation information.
Few-shot learning with improved local representations via bias rectify module
Recent approaches based on metric learning have achieved great progress in few-shot learning.
Reflash Dropout in Image Super-Resolution
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR).
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor.
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution
As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results.
A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds
The recently proposed practical degradation model includes a full spectrum of degradation types, but only considers complex cases that use all degradation types in the degradation process, while ignoring many important corner cases that are common in the real world.
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization
Extensive experiments demonstrate that our simple RepSR is capable of achieving superior performance to previous SR re-parameterization methods among different model sizes.
Evaluating the Generalization Ability of Super-Resolution Networks
However, research on the generalization ability of Super-Resolution (SR) networks is currently absent.
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).
Wireless Deep Video Semantic Transmission
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels.
NTIRE 2022 Challenge on Perceptual Image Quality Assessment
This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods.
A Survey on Collaborative DNN Inference for Edge Intelligence
With the vigorous development of artificial intelligence (AI), the intelligent applications based on deep neural network (DNN) change people's lifestyles and the production efficiency.
PS-NeRV: Patch-wise Stylized Neural Representations for Videos
We study how to represent a video with implicit neural representations (INRs).
Ranked #5 on
Video Reconstruction
on UVG
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information
It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data.
Towards Arbitrary Text-driven Image Manipulation via Space Alignment
Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description.
ITstyler: Image-optimized Text-based Style Transfer
Text-based style transfer is a newly-emerging research topic that uses text information instead of style image to guide the transfer process, significantly extending the application scenario of style transfer.
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels
Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost.
TextIR: A Simple Framework for Text-based Editable Image Restoration
We use the text-image feature compatibility of the CLIP to alleviate the difficulty of fusing text and image features.
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System
However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation.
GET3D--: Learning GET3D from Unconstrained Image Collections
While generative models have shown potential in creating 3D textured shapes from 2D images, their applicability in 3D industries is limited due to the lack of a well-defined camera distribution in real-world scenarios, resulting in low-quality shapes.
Novel Online-Offline MA2C-DDPG for Efficient Spectrum Allocation and Trajectory Optimization in Dynamic Spectrum Sharing UAV Networks
Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications.
Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
The unmanned aerial vehicle (UAV) network is popular these years due to its various applications.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability.
Unifying Image Processing as Visual Prompting Question Answering
To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc.
UAV Trajectory Tracking via RNN-enhanced IMM-KF with ADS-B Data
Therefore, we employ the ADS-B for UAV trajectory tracking in this work.
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.
