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- Xinghao DingSuggested from co-authorship
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Works196 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.
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- 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.
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- Underwater image enhancement with global–local networks and compressed-histogram equalization228
This work proposes a two-branch network to compensate the global distorted color and local reduced contrast, respectively, and designs a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training.
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- 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.
- Spatial-Frequency Domain Information Integration for Pan-Sharpening158
This paper proposes a Spatial-Frequency Information Integration Network, dubbed as SFIIN, which employs the standard convolution to integrate the local information of two modalities of PAN and MS images in the spatial domain and adopts deep Fourier transformation to achieve the image-wide receptive field for exploring global contextual information.
- 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.
- RETRACTED: Pan-Sharpening with Customized Transformer and Invertible Neural Network145
This is the first attempt to introduce transformer and invertible neural network into pan-sharpening field and outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops.
- 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.
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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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