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04/10/2023
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
Single-frame infrared small target (SIRST) detection aims at separating ...
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03/31/2023
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
In this paper, we introduce a new approach to address the challenge of g...
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01/04/2022
MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution
Infrared small target super-resolution (SR) aims to recover reliable and...
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11/25/2021
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
Satellite video cameras can provide continuous observation for a large-s...
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06/01/2021
Dense Nested Attention Network for Infrared Small Target Detection
Single-frame infrared small target (SIRST) detection aims at separating ...
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09/16/2020
Parallax Attention for Unsupervised Stereo Correspondence Learning
Stereo image pairs encode 3D scene cues into stereo correspondences betw...
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06/17/2020
Learning Sparse Masks for Efficient Image Super-Resolution
Current CNN-based super-resolution (SR) methods process all locations eq...
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04/08/2020
Learning for Scale-Arbitrary Super-Resolution from Scale-Specific Networks
Recently, the performance of single image super-resolution (SR) has been...
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01/06/2020
Deep Video Super-Resolution using HR Optical Flow Estimation
Video super-resolution (SR) aims at generating a sequence of high-resolu...
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03/14/2019
Learning Parallax Attention for Stereo Image Super-Resolution
Stereo image pairs can be used to improve the performance of super-resol...
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09/23/2018
Learning for Video Super-Resolution through HR Optical Flow Estimation
Video super-resolution (SR) aims to generate a sequence of high-resoluti...
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08/23/2017
Fast single image super-resolution based on sigmoid transformation
Single image super-resolution aims to generate a high-resolution image f...
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06/20/2017
