J
Junjun Jiang
Researcher at Harbin Institute of Technology
Publications - 248
Citations - 10584
Junjun Jiang is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Face hallucination. The author has an hindex of 39, co-authored 193 publications receiving 5429 citations. Previous affiliations of Junjun Jiang include Wuhan University & China University of Geosciences (Wuhan).
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FusionGAN: A generative adversarial network for infrared and visible image fusion
TL;DR: This paper proposes a novel method to fuse two types of information using a generative adversarial network, termed as FusionGAN, which establishes an adversarial game between a generator and a discriminator, where the generator aims to generate a fused image with major infrared intensities together with additional visible gradients.
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Image Matching from Handcrafted to Deep Features: A Survey
TL;DR: This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
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DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion
TL;DR: A new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions, which establishes an adversarial game between a generator and two discriminators.
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Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming
TL;DR: This paper proposes a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images, which outperforms current state-of-the-art methods, particularly in the case of severe outliers.
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Locality Preserving Matching
TL;DR: The authors' method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds, and achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.