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Found 18 papers, 3 papers with code
Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring
This paper tackles the problem of motion deblurring of dynamic scenes.
CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models
On the other hand, a more flexible latent space can capture intricate facial details better, but is extremely difficult to optimize for highly degraded faces using existing techniques.
Unsupervised network for low-light enhancement
Different ablation studies show the importance of PAM and CIN in improving the visible quality of the image.
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images
Both inversion of real images and determination of controllable latent directions are computationally expensive operations.
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation
Our model fuses per-pixel local information learned using two fully convolutional depth encoders with global contextual information learned by a transformer encoder at different scales.
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.
Dynamic Scene Video Deblurring using Non-Local Attention
This paper tackles the challenging problem of video deblurring.
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.
Gated Spatio-Temporal Attention-Guided Video Deblurring
Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur.
Distillation-Guided Image Inpainting
Image inpainting methods have shown significant improvements by using deep neural networks recently.
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.
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).
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.
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 #34 on
Image Deblurring
on GoPro
(using extra training data)
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 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).
