Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Luc Van GoolSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works542 from public data
- SwinIR: Image Restoration Using Swin Transformer5,285
A strong baseline model SwinIR is proposed for image restoration based on the Swin Transformer that outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced byUp to 67%.
- 4,242
- RePaint: Inpainting using Denoising Diffusion Probabilistic Models2,341
This work proposes RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks and outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.
- 1,955
- A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution1,516
This work proposes A+, an improved variant of Anchored Neighborhood Regression, which combines the best qualities of ANR and SF and builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF.
- Learning Discriminative Model Prediction for Tracking1,436
An end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction, derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations.
- 1,351
- Plug-and-Play Image Restoration With Deep Denoiser Prior1,296
Experimental results demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state- of theart learning- based methods.
- Designing a Practical Degradation Model for Deep Blind Image Super-Resolution1,035
The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications.
- CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion990
A novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network that achieves promising results in multiple fusion tasks, including infrared- visible image fusion and medical image fusion, and can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark.
- Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks959
A deep learning solution to age estimation from a single face image without the use of facial landmarks is proposed and the IMDB-WIKI dataset is introduced, the largest public dataset of face images with age and gender labels.
- Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement884
A simple yet principled One-stage Retinex-based Framework (ORF), designed an Illumination-Guided Transformer (IGT) that utilizes illumination representations to direct the modeling of non-local interactions of regions with different lighting conditions, and obtains the algorithm, Retinexformer.
- O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images782
The first outdoor scenes database composed of pairs of real hazy and corresponding haze-free images is introduced, and is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000.
- 769
- Probabilistic Regression for Visual Tracking751
This work proposes a probabilistic regression formulation and applies it to tracking, which is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task and substantially improves the performance.
- Generative Adversarial Networks for Extreme Learned Image Compression686
If a semantic label map of the original image is available, the learned image compression system can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost.
- Deep Unfolding Network for Image Super-Resolution683
This paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning- based methods.
- 646
- 623
- DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks594
An end-to-end deep learning approach that bridges the gap by translating ordinary photos into DSLR-quality images by learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness.
- The 2018 PIRM Challenge on Perceptual Image Super-Resolution592
This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018, and concludes with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
- Conditional Probability Models for Deep Image Compression548
This paper proposes a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto- Encoder.
- Denoising Diffusion Models for Plug-and-Play Image Restoration511
Experimental results on three representative IR tasks demonstrate that DiffPIR achieves state-of-the-art performance on both the FFHQ and ImageNet datasets in terms of reconstruction faithfulness and perceptual quality with no more than 100 NFEs.
- NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images499
This work presents an objective assessment of several state- of-the-art single image dehazing methods that were evaluated using NH-HAZE dataset, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images.
- Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images499
Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes and contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
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 2026-10-14. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.