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05/15/2023
Denoising Diffusion Models for Plug-and-Play Image Restoration
Plug-and-play Image Restoration (IR) has been widely recognized as a fle...
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05/06/2023
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fie...
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03/23/2023
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
Hyperspectral imaging systems that use multispectral filter arrays (MSFA...
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03/23/2023
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor
Pixel binning based Quad sensors have emerged as a promising solution to...
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01/12/2023
Event-Based Frame Interpolation with Ad-hoc Deblurring
The performance of video frame interpolation is inherently correlated wi...
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07/25/2022
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary...
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07/16/2022
Towards Lightweight Super-Resolution with Dual Regression Learning
Deep neural networks have exhibited remarkable performance in image supe...
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06/05/2022
Recurrent Video Restoration Transformer with Guided Deformable Attention
Video restoration aims at restoring multiple high-quality frames from mu...
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03/24/2022
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep ...
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01/28/2022
VRT: A Video Restoration Transformer
Video restoration (e.g., video super-resolution) aims to restore high-qu...
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08/23/2021
SwinIR: Image Restoration Using Swin Transformer
Image restoration is a long-standing low-level vision problem that aims ...
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06/12/2021
Video Super-Resolution Transformer
Video super-resolution (VSR), with the aim to restore a high-resolution ...
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04/12/2021
LocalViT: Bringing Locality to Vision Transformers
We study how to introduce locality mechanisms into vision transformers. ...
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03/13/2021
Internal Wasserstein Distance for Adversarial Attack and Defense
Deep neural networks (DNNs) are vulnerable to adversarial examples that ...
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03/13/2021
Learning Defense Transformers for Counterattacking Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples with ...
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07/28/2020
Improving Generative Adversarial Networks with Local Coordinate Coding
Generative adversarial networks (GANs) have shown remarkable success in ...
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03/31/2020
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
Recently, deep neural networks (DNNs) have made great progress on automa...
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03/16/2020
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Deep neural networks have exhibited promising performance in image super...
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03/07/2020
Generative Low-bitwidth Data Free Quantization
Neural network quantization is an effective way to compress deep models ...
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03/01/2020
Joint Wasserstein Distribution Matching
Joint distribution matching (JDM) problem, which aims to learn bidirecti...
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11/18/2019
Online Adaptive Asymmetric Active Learning with Limited Budgets
Online Active Learning (OAL) aims to manage unlabeled datastream by sele...
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11/03/2019
Multi-marginal Wasserstein GAN
Multiple marginal matching problem aims at learning mappings to match a ...
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09/19/2018
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
Deep neural networks have exhibited promising performance in image super...
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06/13/2018
