Search Results for author:
Found 17 papers, 6 papers with code
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy.
Ranked #8 on
Text-to-Video Generation
on UCF-101
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
In this work, to achieve high average bit-reduction with less accuracy loss, we propose a novel Content-Aware Dynamic Quantization (CADyQ) method for SR networks that allocates optimal bits to local regions and layers adaptively based on the local contents of an input image.
Attentive Fine-Grained Structured Sparsity for Image Restoration
Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models.
Pay Attention to Hidden States for Video Deblurring: Ping-Pong Recurrent Neural Networks and Selective Non-Local Attention
When motion blur is strong, however, hidden states are hard to deliver proper information due to the displacement between different frames.
Recurrence-in-Recurrence Networks for Video Deblurring
State-of-the-art video deblurring methods often adopt recurrent neural networks to model the temporal dependency between the frames.
NTIRE 2021 Challenge on Video Super-Resolution
Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart.
NTIRE 2021 Challenge on Image Deblurring
In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions.
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring
The supervised reblurring loss at training stage compares the amplified blur between the deblurred and the sharp images.
AIM 2020 Challenge on Video Temporal Super-Resolution
Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low.
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low.
NTIRE 2020 Challenge on Image and Video Deblurring
This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring.
Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring
In this work, we aim to improve the accuracy of recurrent models by adapting the hidden states transferred from past frames to the frame being processed so that the relations between video frames could be better used.
Clustering Convolutional Kernels to Compress Deep Neural Networks
In this paper, we propose a novel method to compress CNNs by reconstructing the network from a small set of spatial convolution kernels.
Enhanced Deep Residual Networks for Single Image Super-Resolution
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN).
Ranked #1 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(SSIM metric)
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear.
Ranked #17 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model
We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus in our new blur model.
