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Found 27 papers, 22 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
Deep Video Super-Resolution using HR Optical Flow Estimation
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Ranked #6 on
Video Super-Resolution
on MSU Super-Resolution for Video Compression
(BSQ-rate over ERQA metric)
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
Learning for Video Super-Resolution through HR Optical Flow Estimation
Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.
Ranked #13 on
Video Super-Resolution
on Vid4 - 4x upscaling
Fast single image super-resolution based on sigmoid transformation
Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications.
Multi-frame image super-resolution with fast upscaling technique
In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space.
