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Claim this profileWorks319 from public data
- 1,259
- 1,080
- Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal938
This work introduces a deep network architecture called DerainNet for removing rain streaks from an image based on the deep convolutional neural network (CNN), which directly learns the mapping relationship between rainy and clean image detail layers from data.
- 801
- A fusion-based enhancing method for weakly illuminated images739
A fusion-based method for enhancing various weakly illuminated images that requires only one input to obtain the enhanced image and represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image.
- 579
- Progressive Feature Alignment for Unsupervised Domain Adaptation490
The Progressive Feature Alignment Network (PFAN) is proposed to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain.
- 467
- Lightweight Pyramid Networks for Image Deraining421
This paper finds that by introducing the mature Gaussian–Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters.
- 390
- Harmonizing Transferability and Discriminability for Adapting Object Detectors350
A Hierarchical Transferability Calibration Network (HTCN) that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability is proposed.
- Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes302
A novel text detector namely LOMO is presented, which localizes the text progressively for multiple times (or in other word, LOok More than Once), and the state-of-the-art results on several public benchmarks confirm the striking robustness and effectiveness of LomO.
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- InstantSplat: Sparse-view Gaussian Splatting in Seconds222
The strengths of point-based representations with end-to-end dense stereo models (DUSt3R) with end-to-end dense stereo models (DUSt3R) to tackle the complex yet unresolved issues in NVS under unconstrained settings, which encompasses pose-free and sparse view challenges.
- 214
- Unsupervised Underwater Image Restoration: From a Homology Perspective201
It is demonstrated that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration.
- Few-shot medical image segmentation using a global correlation network with discriminative embedding194
This study constructed a few-shot image segmentation mechanism using a deep convolutional network trained episodically and developed an efficient global correlation module to model the correlation between a support and query image and incorporate it into the deep network.
- Rain Streak Removal via Dual Graph Convolutional Network168
This work proposes a simple yet effective dual graph convolutional network (GCN) for single image rain removal that is easy to implement, end-to-end trainable and computationally efficient and achieves significant improvements over the recent state-of-the-art methods.
- Single-Image-Based Rain and Snow Removal Using Multi-guided Filter154
This paper proposes a new rain and snow removal method through using low frequency part of a single image based on a key difference between clear background edges and rain streaks or snowflakes, which has good performance in rain removal andSnow removal.
- 150
- Residual-Guide Network for Single Image Deraining148
A novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts high-quality reconstruction while using fewer parameters than previous methods is proposed.
- Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI143
A Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled \(k \) -space data is developed, and the proposed regularization framework can improve reconstruction accuracy over other methods.
- An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection140
This work proposes a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data.
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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