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Found 38 papers, 33 papers with code
LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
Despite this complexity, mainstream LF image SR methods typically adopt a deterministic approach, generating only a single output supervised by pixel-wise loss functions.
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
To address this issue and achieve a better trade-off between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation.
Learning Remote Sensing Object Detection with Single Point Supervision
In this paper, we make the first attempt to achieve RS object detection with single point supervision, and propose a PSOD method tailored for RS images.
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4.
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels.
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way.
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images.
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation
However, the disparity map is only a sub-space projection (i. e., an expectation) of the disparity distribution, which is essential for models to learn.
Real-World Light Field Image Super-Resolution via Degradation Modulation
In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images.
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results.
Occlusion-Aware Cost Constructor for Light Field Depth Estimation
Based on the proposed cost constructor, we develop a deep network for LF depth estimation.
Disentangling Light Fields for Super-Resolution and Disparity Estimation
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution
Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets).
Learnable Lookup Table for Neural Network Quantization
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.
Dense Dual-Attention Network for Light Field Image Super-Resolution
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available.
DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images
Rotated object detection in aerial images has received increasing attention for a wide range of applications.
A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field.
Light Field Image Super-Resolution with Transformers
With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted.
Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution
In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays.
Dense Nested Attention Network for Infrared Small Target Detection
With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection.
Unsupervised Degradation Representation Learning for Blind Super-Resolution
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation
In this paper, we explore the potential benefits introduced by image SR to ship detection, and propose an end-to-end network named ShipSRDet.
Arbitrary-Oriented Ship Detection through Center-Head Point Extraction
Moreover, we introduce a new dataset for multi-class arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) which is named FGSD2021.
Symmetric Parallax Attention for Stereo Image Super-Resolution
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.
Parallax Attention for Unsupervised Stereo Correspondence Learning
Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.
Light Field Image Super-Resolution Using Deformable Convolution
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
Spatial-Angular Attention Network for Light Field Reconstruction
In this paper, we propose a spatial-angular attention network to perceive correspondences in the light field non-locally, and reconstruction high angular resolution light field in an end-to-end manner.
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
Learning A Single Network for Scale-Arbitrary Super-Resolution
In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.
Deformable 3D Convolution for Video Super-Resolution
In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR.
Spatial-Angular Interaction for Light Field Image Super-Resolution
Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.
Learning Parallax Attention for Stereo Image Super-Resolution
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.
Ranked #1 on
Image Super-Resolution
on KITTI 2012 - 4x upscaling
