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- Image Super-Resolution Using Deep Convolutional Networks10,147
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images, represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one.
- Learning a Deep Convolutional Network for Image Super-Resolution5,839
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
- ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks4,943
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
- Accelerating the Super-Resolution Convolutional Neural Network3,673
This paper aims at accelerating the current SRCNN, and proposes a compact hourglass-shape CNN structure for faster and better SR, and presents the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance.
- Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data2,160
This work extends the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data and employs a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics.
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- EDVR: Video Restoration With Enhanced Deformable Convolutional Networks1,341
This work proposes a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, and proposes a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.
- Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform1,224
It is shown that it is possible to recover textures faithful to semantic classes in a single network conditioned on semantic segmentation probability maps through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation.
- Activating More Pixels in Image Super-Resolution Transformer1,170
A novel Hybrid Attention Transformer (HAT) is proposed, which combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability.
- Compression Artifacts Reduction by a Deep Convolutional Network942
A compact and efficient network for seamless attenuation of different compression artifacts is formulated and it is demonstrated that a deeper model can be effectively trained with the features learned in a shallow network.
- BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond698
A succinct pipeline is shown that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms and can serve as strong baselines for future VSR approaches.
- Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks619
This work proposes a Cycle-in-Cycle network structure with generative adversarial networks (GAN) as the basic component to tackle the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
- Blind Super-Resolution With Iterative Kernel Correction591
An iterative correction scheme -- IKC that achieves better results than direct kernel estimation in blind SR problem and an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD.
- Efficient Image Super-Resolution Using Pixel Attention474
This work designs a lightweight convolutional neural network for image super resolution with a newly proposed pixel attention scheme that could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters.
- RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution412
This work first train a Ranker which can learn the behavior of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality, and shows that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics.
- Interpreting Super-Resolution Networks with Local Attribution Maps381
This work proposes a novel attribution approach called local attribution map (LAM), which inherits the integral gradient method yet with two unique features: one is to use the blurred image as the baseline input, and the other is to adopt the progressive blurring function as the path function.
- Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild372
This work introduces SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up, and develops a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration.
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- PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration320
It is indicated that existing IQA methods cannot fairly evaluate GAN-based IR algorithms, and a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) is contributed, which includes the results of GAn-based methods, which are missing in previous datasets.
- BeautyGAN283
A dual input/output Generative Adversarial Network that enables the network to learn translation on instance-level through unsupervised adversarial learning, and could generate visually pleasant makeup faces and accurate transferring results.
- Blueprint Separable Residual Network for Efficient Image Super-Resolution272
This paper proposes Blueprint Separable Residual Network (BSRN), a model containing two efficient designs, one of which is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation.
- Video super-resolution based on deep learning: a comprehensive survey235
This work comprehensively investigates 37 state-of-the-art VSR methods based on deep learning and proposes a taxonomy and classify the methods into seven sub-categories according to the ways of utilizing inter-frame information.
- Blind Image Super-Resolution: A Survey and Beyond228
A taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used to solve the SR model is proposed, which helps summarize and distinguish among existing methods.
- ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic223
A new solution pipeline that combines classification and SR in a unified framework that can help most existing methods save up to 50% FLOPs on DIV8K datasets and introduces a new classification method with two losses – Class-Loss and Average- Loss to produce the classification results.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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