Search Results for author:
Found 17 papers, 13 papers with code
Denoising Diffusion Models for Plug-and-Play Image Restoration
Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.
Event-Based Frame Interpolation with Ad-hoc Deblurring
The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene.
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising
To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
Ranked #1 on
Video Denoising
on VideoLQ
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images.
Towards Interpretable Video Super-Resolution via Alternating Optimization
These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
VRT: A Video Restoration Transformer
Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
Ranked #1 on
Deblurring
on BASED
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.
Ranked #3 on
Deblurring
on GoPro
(using extra training data)
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
Ranked #2 on
Color Image Denoising
on urban100 sigma15
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
Boosting Few-shot Semantic Segmentation with Transformers
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention.
Video Super-Resolution Transformer
Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.
Flow-based Kernel Prior with Application to Blind Super-Resolution
Kernel estimation is generally one of the key problems for blind image super-resolution (SR).
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition.
