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Found 26 papers, 10 papers with code
LAN: Learning to Adapt Noise for Image Denoising
Removing noise from images, a. k. a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments.
Deep Variational Bayesian Modeling of Haze Degradation Process
To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing.
Harnessing Meta-Learning for Improving Full-Frame Video Stabilization
Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task.
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution
Single image super-resolution (SISR) has experienced significant advancements, primarily driven by deep convolutional networks.
NoiseTransfer: Image Noise Generation with Contrastive Embeddings
Although several real-world noisy datasets have been presented, the number of train datasets (i. e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive.
Semantic-Aware Dynamic Parameter for Video Inpainting Transformer
Recent learning-based video inpainting approaches have achieved considerable progress.
Task Agnostic Restoration of Natural Video Dynamics
In many video restoration/translation tasks, image processing operations are na\"ively extended to the video domain by processing each frame independently, disregarding the temporal connection of the video frames.
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods.
Restore from Restored: Single-image Inpainting
Recent image inpainting methods have shown promising results due to the power of deep learning, which can explore external information available from the large training dataset.
Self-Supervised Adaptation for Video Super-Resolution
Recent single-image super-resolution (SISR) networks, which can adapt their network parameters to specific input images, have shown promising results by exploiting the information available within the input data as well as large external datasets.
Restore from Restored: Single-image Inpainting
Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset.
Progressive Image Super-Resolution via Neural Differential Equation
We propose a new approach for the image super-resolution (SR) task that progressively restores a high-resolution (HR) image from an input low-resolution (LR) image on the basis of a neural ordinary differential equation.
Deep Motion Blind Video Stabilization
Despite the advances in the field of generative models in computer vision, video stabilization still lacks a pure regressive deep-learning-based formulation.
Scene-Adaptive Video Frame Interpolation via Meta-Learning
Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
Restore from Restored: Single Image Denoising with Pseudo Clean Image
However, these methods have limitations in using internal information available in a given test image.
Restore from Restored: Video Restoration with Pseudo Clean Video
We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.
Fast Adaptation to Super-Resolution Networks via Meta-Learning
In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time.
Fast and Full-Resolution Light Field Deblurring using a Deep Neural Network
Restoring a sharp light field image from its blurry input has become essential due to the increasing popularity of parallax-based image processing.
Spatio-temporal Transformer Network for Video Restoration
State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information.
Online Video Deblurring via Dynamic Temporal Blending Network
We show the superiority of the proposed method in an extensive experimental evaluation.
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 #18 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Occlusion-Aware Video Deblurring with a New Layered Blur Model
We also provide a novel analysis on the blur kernel at object boundaries, which shows the distinctive characteristics of the blur kernel that cannot be captured by conventional blur models.
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.
Generalized Video Deblurring for Dynamic Scenes
We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods.
Segmentation-Free Dynamic Scene Deblurring
Thus, we propose a new energy model simultaneously estimating motion flow and the latent image based on robust total variation (TV)-L1 model.
