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Claim this profileWorks129 from public data
- Multi-scale Residual Network for Image Super-Resolution899
A novel multi-scale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods.
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- Structure-Preserving Deraining with Residue Channel Prior Guidance180
A Structure-Preserving Deraining Network (SPDNet) with RCP guidance is proposed, which directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions.
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- MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution123
A Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time and achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors.
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- Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction76
This work proposes a Cascaded Dilated Dense Network (CDDN) for MRI reconstruction and converts the complex result from first DC operation to real-valued images and applied another sampled \emph{k}-space data replacement.
- Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation70
A Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the stereo image super-resolution and disparity estimation in a unified framework and interact them with each other to further improve their performance.
- Single Image Dehazing and Denoising: A Fast Variational Approach67
Extensive experimental results on real scenes demonstrate that the proposed fast variational approach to dehaze and denoise simultaneously can restore vivid and contrastive haze- and noise-free images effectively.
- Efficient and Accurate Multi-Scale Topological Network for Single Image Dehazing58
A multi-scale topological network that provides a large number of search paths that enable the network to extract abundant image features as well as strong fault tolerance and robustness and adaptively select important features and ignore interference information when fusing different scale representations is proposed.
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- Enhanced Sparse Model for Blind Deblurring43
Comprehensive evaluations on public datasets and real-world images demonstrate the superiority of the proposed method against state-of-the-art methods in terms of both speed and accuracy.
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- Deep Richardson–Lucy Deconvolution for Low-Light Image Deblurring38
A data-driven approach to model the saturated pixels by a learned latent map is developed and performs favorably against state-of-the-art algorithms both quantitatively and qualitatively on synthetic and real-world images.
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- OID: Outlier Identifying and Discarding in Blind Image Deblurring30
This paper develops a simple yet effective Outlier Identifying and Discard-ing (OID) method, which alleviates limitations in existing Maximum A Posteriori (MAP)-based deblurring models when significant outliers are presented.
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- Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation15
The degradation mechanism of the noisy image is explored and a lightweight Multiple Degradation and Reconstruction Network (MDRN) is proposed to progressively remove noise to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions.
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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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