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Found 14 papers, 8 papers with code
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation
This positions our approach as a versatile tool for various burst image processing applications.
High Dynamic Range and Super-Resolution from Raw Image Bursts
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts
This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time.
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.
Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
Adversarially Learned Anomaly Detection
Anomaly detection is a significant and hence well-studied problem.
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
GANS are powerful generative models that are able to model the manifold of natural images.
Efficient GAN-Based Anomaly Detection
However, few works have explored the use of GANs for the anomaly detection task.
