Search Results for author:
Found 20 papers, 5 papers with code
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
For training, only one set of source input images is therefore provided in the challenge.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
Bringing Alive Blurred Moments
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
Ranked #36 on
Image Deblurring
on GoPro
(using extra training data)
Region-Adaptive Dense Network for Efficient Motion Deblurring
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur.
Motion Deblurring with an Adaptive Network
In this paper, we address the problem of dynamic scene deblurring in the presence of motion blur.
Ranked #25 on
Image Deblurring
on GoPro
(using extra training data)
Planar Geometry and Image Recovery from Motion-Blur
We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation.
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring
Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comes at the expense of of the increase in model size and inference speed.
Ranked #26 on
Image Deblurring
on GoPro
(using extra training data)
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
Spatially-Adaptive Image Restoration using Distortion-Guided Networks
However, we hypothesize that such spatially rigid processing is suboptimal for simultaneously restoring the degraded pixels as well as reconstructing the clean regions of the image.
Distillation-Guided Image Inpainting
Image inpainting methods have shown significant improvements by using deep neural networks recently.
Mitigating Channel-wise Noise for Single Image Super Resolution
In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches.
Image Restoration using Feature-guidance
Image restoration is the task of recovering a clean image from a degraded version.
Adaptive Image Inpainting
We deploy cross and self distillation techniques and discuss the need for a dedicated completion-block in encoder to achieve the distillation target.
Unfolding a blurred image
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
Image Superresolution using Scale-Recurrent Dense Network
In this paper, we propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs)) that allow extraction of abundant local features from the image.
Deep Networks for Image and Video Super-Resolution
To enable super-resolution for multiple factors, we propose a scale-recurrent framework which reutilizes the filters learnt for lower scale factors recursively for higher factors.
Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring
This paper tackles the problem of motion deblurring of dynamic scenes.
