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Works388 from public data
- FusionGAN: A generative adversarial network for infrared and visible image fusion2,265
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.
- 1,341
- Image Matching from Handcrafted to Deep Features: A Survey988
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.
- Multi-Scale Progressive Fusion Network for Single Image Deraining859
This work explores the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi- scale progressive fusion network (MSPFN) for single image rain streak removal.
- 831
- FusionDN: A Unified Densely Connected Network for Image Fusion658
A new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN, which obtains a single model applicable to multiple fusion tasks by applying elastic weight consolidation to avoid forgetting what has been learned from previous tasks when training multiple tasks sequentially.
- Locality Preserving Matching601
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.
- 544
- 474
- Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion471
This work proposes a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training and has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.
- Infrared and visible image fusion via detail preserving adversarial learning456
This paper proposes an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning that is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods.
- 332
- BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation318
A novel framework called BinsFormer, tailored for the classification-regression-based depth estimation, which mainly focuses on two crucial components in the specific task: proper generation of adaptive bins and sufficient interaction between probability distribution and bins predictions.
- 283
- DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation270
A hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features and model the affinity between the heterogeneous features in a set-to-set translation manner and surpasses state-of-the-art monocular depth estimation methods with prominent margins.
- Hierarchical dense recursive network for image super-resolution242
A novel hierarchical dense connection network (HDN) is advocated for image SR that outperforms the state-of-the-art methods in terms of quantitative indicators and realistic visual effects, as well as enjoys a fast and accurate reconstruction.
- 235
- 217
- Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon214
A new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting is studied.
- 204
- Hyperspectral Image Classification in the Presence of Noisy Labels203
Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of the proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive.
- 190
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- Deep Learning-based Face Super-resolution: A Survey166
This survey presents a comprehensive review of deep learning-based FSR methods in a systematic manner and roughly categorizes existing methods according to the utilization of facial characteristics.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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