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Claim this profileWorks132 from public data
- 2,961
- Kindling the Darkness1,856
This work builds a simple yet effective network for Kindling the Darkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components that are robust against severe visual defects, and user-friendly to arbitrarily adjust light levels.
- Beyond Brightening Low-light Images1,056
This work builds a simple yet effective network, which, inspired by Retinex theory, decomposes images into two components, followed a divide-and-conquer principle, and is trained with paired images shot under different exposure conditions.
- 951
- 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.
- Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity641
This paper unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images, and defines a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks.
- 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.
- A review of multimodal image matching: Methods and applications532
This survey provides a comprehensive review of multimodal image matching methods from handcrafted to deep methods for each research field according to their imaging nature, including medical, remote sensing and computer vision.
- 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.
- 380
- Single Image Deraining: A Comprehensive Benchmark Analysis371
A comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images, and provides a rich variety of criteria for dehazing algorithm evaluation.
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- Low-light Image Enhancement via Breaking Down the Darkness272
A novel framework inspired by the divide-and-rule principle is presented, greatly alleviating the degradation entanglement of images captured in low-light environments by converting an image from the RGB colorspace into a luminance-chrominance one.
- A feature selection algorithm of decision tree based on feature weight262
Experimental results show that FWDT the authors' proposed method performs better for the measures of accuracy, recall and F1-score and it can reduce the required time in constructing the decision tree.
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- 185
- Multi-view subspace clustering with intactness-aware similarity181
A novel multi-view subspace clustering model that attempts to form an informative intactness-aware similarity based on the intact space learning technique is proposed and its superior performance over other state-of-the-art alternatives is revealed.
- 179
- LIME135
This paper proposes a very simple and effective method, named as LIME, to enhance low-light images, where the illumination of each pixel is first estimated individually by finding the maximum value in R, G and B channels.
- 126
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 2026-10-14. 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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