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Works16 from public data
- 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.
- SRFlow: Learning the Super-Resolution Space with Normalizing Flow444
The proposed SRFlow is a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input, and directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
- Unsupervised Learning for Real-World Super-Resolution195
This work learns to invert the effects of bicubic downsampling in order to restore the natural image characteristics present in the data, and can be trained with direct pixel-wise supervision in the high resolution domain, while robustly generalizing to real input.
- NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results191
The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
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- Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling131
The hierarchical conditional flow (HCFlow) is proposed as a unified framework for image SR and image rescaling and achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.
- AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results110
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
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- DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows55
DeFlow, a method for learning stochastic image degradations from unpaired data based on a novel unpaired learning formulation for conditional normalizing flows, is proposed, which allows to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions.
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- Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution16
This work revisiting the L1 loss and showing that it corresponds to a one-layer conditional flow is shown to outperform state-of-the-art methods for photo-realistic super-resolution and demonstrate that the flexibility of deeper flows leads to better visual quality and consistency when combined with adversarial losses.
- ReBotNet: Fast Real-Time Video Enhancement5
This work designs an efficient and fast framework to perform real-time video enhancement for practical use-cases like live video calls and video streams and shows extensive results on multiple datasets where ReBot-Net outperforms existing approaches with lower computations, reduced memory requirements, and faster inference time.
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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 2026-10-16. 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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