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- 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.
- Interpreting the Latent Space of GANs for Semantic Face Editing1,275
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.
- Blind Super-Resolution With Iterative Kernel Correction590
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.
- Dual Aggregation Transformer for Image Super-Resolution454
A novel Transformer model, Dual Aggregation Transformer (DAT), is proposed, which aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner.
- 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.
- Image Processing Using Multi-Code GAN Prior347
A novel approach is proposed, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety of image processing tasks, by employing multiple latent codes to generate multiple feature maps at some intermediate layer of the generator and composing them with adaptive channel importance to recover the input image.
- 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.
- 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.
- 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.
- Teaching Large Language Models to Regress Accurate Image Quality Scores Using Score Distribution183
The distribution-based Depicted image Quality Assessment model for Score regression (DeQA-Score) is developed, showing that DeQA-Score stably outperforms baselines in score regression and can predict the score distribution that closely aligns with human annotations.
- Accurate Image Restoration with Attention Retractable Transformer169
Attention Retractable Transformer (ART) is proposed for image restoration, which presents both dense and sparse attention modules in the network, which greatly enhances representation ability of Transformer while providing retractable attention on the input image.
- Masked Image Training for Generalizable Deep Image Denoising143
This work presents a novel approach to enhance the generalization performance of denoising networks, known as masked training, which involves masking random pixels of the input image and reconstructing the missing information during training.
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- AI-enabled image fraud in scientific publications100
It is shown that such advanced generative models threaten the publishing system in academia as they may be used to generate fake scientific images that cannot be effectively identified and arouses the vigilance of the scientific community onfake scientific images generated by artificial intelligence (AI) models.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
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- NTIRE 2021 Challenge on Perceptual Image Quality Assessment55
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, achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
- Suppressing Model Overfitting for Image Super-Resolution Networks52
A simple learning principle MixUp is introduced to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples and this strategy proves to be successful in reducing the biases of data.
- Super Resolution Perception for Smart Meter Data51
The problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data is presented and it is demonstrated that the reconstructed high-frequency data can lead to better appliance identification results.
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- Enhancing Descriptive Image Quality Assessment With a Large-Scale Multi-Modal Dataset45
Experimental results demonstrate that EDQA significantly outperforms traditional score-based methods, prior VLM-based IQA models, and proprietary GPT-4V in distortion identification, instant rating, and reasoning tasks.
- Harnessing Diffusion-Yielded Score Priors for Image Restoration45
HYPIR outperforms previous state-of-the-art methods, achieving efficient and high-quality image restoration, and inherits the capabilities of diffusion models with rich user control, enabling text-guided restoration and adjustable texture richness.
- Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution.42
This paper proposes the Trinity Enhancement Network (TENet), a specially designed learning-based method for the mixture problem, which adopts a novel image processing pipeline order and a joint learning strategy, and contributes a new dataset namely PixelShift200, which consists of high-quality full color sampled real-world images using the advanced pixel shift technique.
- NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results41
This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video and proposed the LDV 2.0 dataset, which includes theLDV dataset (240 videos) and 95 additional videos.
- Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning37
This work introduces a novel “Low-Res Leads the Way” (LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images.
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- Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation36
The problem of improving data completeness by recovering high- frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem and a novel machine-learning-based SRP approach is thereafter proposed.
- Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline35
This work comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions, and proposes an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves the state-of-the-art performance for the mixture Problem.
- Evaluating the Generalization Ability of Super-Resolution Networks34
This work makes the first attempt to propose a Generalization Assessment Index for Super-Resolution (SR) networks, namely SRGA, which exploits the statistical characteristics of the internal features of deep networks to measure the generalization ability.
- Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution31
This work introduces a novel approach to craft training degradation distributions using a small set of reference images based upon the binned representation of the degradation space and the Fr\'echet distance between degradation distributions.
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- AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results24
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020 and gauges the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM.
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- Interpreting Low-Level Vision Models With Causal Effect Maps20
This work introduces causality theory to interpret low-level vision models and proposes a model-/task-agnostic method called Causal Effect Map (CEM), which can visualize and quantify the input-output relationships on either positive or negative effects.
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- Revisiting the generalization problem of low-level vision models through the lens of image deraining–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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